This is the code that was used for our approach on predicting judgments (whether a person or a group agrees or disagrees) with an assertion. The paper can be found here: http://www.aclweb.org/anthology/S18-2026

If you want to use the code, please cite:

@inproceedings{wojatzki2018agree,
  title={Agree or Disagree: Predicting Judgments on Nuanced Assertions},
  author={Wojatzki, Michael and Zesch, Torsten and Mohammad, Saif M. and Kiritchenko, Svetlana},
  booktitle={Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  pages={214--224},
  year={2018}
}

This work is part of a larger project on understanding controversial issues. See: https://sites.google.com/view/you-on-issues/about or http://www.lrec-conf.org/proceedings/lrec2018/pdf/321.pdf If you are interested in the data, you can access it on the project's website. In that case, please cite:

@inproceedings{wojatzki2018qqd,
  title={Quantifying Qualitative Data for Understanding Controversial Issues},
  author={Wojatzki, Michael and Mohammad, Saif and Zesch, Torsten and Kiritchenko, Svetlana},
  booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  pages={1405--1418},
  year={2018}
}

For the machine learning approaches we rely on the machine learning framework DKPro TC (https://dkpro.github.io/dkpro-tc/), so the code is mostly written in java (except for the tensorflow experiments, that are written in python). The project uses Java 1.8. You need to set a DKPRO_HOME variable as described here https://zoidberg.ukp.informatik.tu-darmstadt.de/jenkins/job/DKPro%20TC%20Documentation%20(GitHub)/org.dkpro.tc%24dkpro-tc-doc/doclinks/1/#QuickStart

If you have any questions on the set-up please message me!

License

This work is licensed under Attribution-NonCommercial 2.0 Generic license (CC BY-NC 2.0) (https://creativecommons.org/licenses/by-nc/2.0/)

You are free to:

• Share — copy and redistribute the material in any medium or format

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