A Multi-relational Graph-Based Twitter Account Detection Benchmark
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Updated
Jun 9, 2023 - Python
A Multi-relational Graph-Based Twitter Account Detection Benchmark
Multi-dataset stance detection and robustness experiments
Official resource of the paper "Knowledge Enhanced Masked Language Model for Stance Detection", NAACL 2021
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
Final project for NLP(DATA130006) in Fudan university.
MoLE: Cross-Domain Label-Adaptive Stance Detection
Code and data for EMNLP '17 paper "Human Centered NLP with User-Factor Adaptation"
Dataset associated with the "Stance Prediction for Contemporary Issues: Data and Experiments" SocialNLP@ACL2020 paper
an approach based on deep ensemble learning
[DASFAA2023] Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies (code&data)
The PyTorch codebase for the NAACL 2021 paper - "tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets"
Stance and Gender Detection in Tweets on Catalan Independence
[ACL 2022] JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
The Pytorch code for baselines and dataset accompanying the NAACL 2021 paper - "tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets"
This repository contains the data and source code used in NAACL 2021 main conference paper Adversarial Learning for Zero-Shot Stance Detection on Social Media.
This is a transformer based implementation for Stance Detection with Bidirectional Conditional Encoding
a stance detection model based on pre-trained language model
Code for using BERT embeddings and fine-tuning for classification tasks
The purpose of this repository is to demonstrate how to use NLP explanation/interpretability tools.
A simple python script to extract data from Twitter API on a particular target and classify the tweets as positive or negative or neutral based on their sentiment.
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