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The fork was moved to https://github.com/shalmolighosh/bert/ and the BERT.ipynb file has been updated to reflect the same |
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| CNN | ||
| KDEY | ||
| Preprocessing | ||
| SEN-SVM | ||
| TAN | ||
| .gitignore | ||
| BERT.ipynb | ||
| README.md | ||
| Stance-detection-comparison-CLEF2019.pdf | ||
This is the code repository for the paper "Stance Detection in Web and Social Media: A Comparative Study" published in the proceedings of CLEF 2019 conference (http://clef2019.clef-initiative.eu/).
Overview of Code
- Preprocessing: This folder contains the preprocessing code as mentioned in our paper and the datasets. The folder contains all instructions to run the code.
- CNN: This is a simple Kim's CNN based model applied for stance detection. The respective folder has the instructions on how to run the codes
- KDEY: This folder contains our attempt at implementing the approach given in the paper : Twitter Stance Detection — A Subjectivity and Sentiment Polarity Inspired Two-Phase Approach link.The folder contains all instructions to run the code
- TAN: This folder contains the codes for TAN and LSTM, the details of which are mentioned in our paper. The folder contains all instructions to run the code.
- SEN-SVM: This folder contains the codes for the SEN-SVM method, the details of which are mentioned in our paper. The folder contains all instructions to run the code.
- Bert.ipynb: This ipython notebook contains the code used for running BERT on the dataset. This needs to be be run on Google Colab with TPU support.
Publication
If you use these codes, please cite our paper:
@inproceedings{
title={{Stance Detection in Web and Social Media: A Comparative Study}},
author={Ghosh, Shalmoli and Singhania, Prajwal and Singh, Siddharth and Rudra, Koustav and Ghosh, Saptarshi},
booktitle={{Proc. Conference and Labs of the Evaluation Forum (CLEF)}},
year={2019}}