Just assume this is my toy for learning pytorch for the first time (it's easy and definitely awesome!). In this repo you can find the implementation of both char-rnn and word-rnn to do sentiment analysis based on twitter data.
Not only sentiment analysis, you can also use this project as a sentence classification with multiple classes. Just put your class ids on the csv and you're good to go!
- You can choose between LSTM and CNN-LSTM for the character decoder
- Each batches will be grouped in respect of their lengths
- For current implementation, the dataset is set to tokenize the input based on characters. There is still no way to update the tokenization via config.
- Still no way to update RNN cell from config.
- Still no way to update optimizer from config.
- Install pytorch
- Run
pip install -r requirements.txt
Run python run.py with the following options
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS Number of epochs
--dataset DATASET Path for your training, validation and test dataset.
As this package uses torch text to load the data,
please follow the format by providing the path and
filename without its extension
--batch_size BATCH_SIZE
The number of batch size for every step
--log_interval LOG_INTERVAL
--save_interval SAVE_INTERVAL
--validation_interval VALIDATION_INTERVAL
--char_level CHAR_LEVEL
Whether to use the model with character level or word
level embedding. Specify the option if you want to use
character level embedding
--model_config MODEL_CONFIG
Location of model config
--model_dir MODEL_DIR
Location to save the model
This is the example of how you can run it
python run.py --model_config config/cnn_rnn.yml --epochs 50 --model_dir models --dataset data/sentiment
You can download the raw data from [1]. It contains 1,578,627 classified tweets, each row is classified as 1 for positive sentiment and 0 for negative sentiment. Kudos to [2] for providing the link to the data!. However, the data provided by [1] have 4 columns, while on this code we only need the text and the sentiment only, you can convert the data first by grabbing the first and the last columns before feeding into the algorithm. For the alternative, you can also download the data from [4], this contains the same number of data as the original, but I have already cleaned it up a bit and you can run the code without any further modification.
Want to run with your own data? No problem, create csv files for training and testing with two columns, the first one being the sentiment and the second being the text. Don't forget to use the same name for both files and differentiate it with suffix .train and .test.
[1] http://thinknook.com/wp-content/uploads/2012/09/Sentiment-Analysis-Dataset.zip
[2] http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/
[3] https://karpathy.github.io/2015/05/21/rnn-effectiveness/
[4] https://drive.google.com/file/d/1-1QNrYebNxge9vMP7YJJceehQkZtqAcO