The package implements WEKA filters for calculating state-of-the-art affective analysis features from tweets that can be fed into machine learning algorithms. Many of these features were drawn from the NRC-Canada System. It also implements methods for building affective lexicons and distant supervision methods for training affective models from unlabelled tweets.
Five participating teams used AffectiveTweets in WASSA-2017 to generate feature vectors, including the teams that eventually ranked first, second, and third. For SemEval-2018, the package was used by 15 teams.
The most relevant papers on which this package is based are:
- Sentiment Analysis of Short Informal Texts. Svetlana Kiritchenko, Xiaodan Zhu and Saif Mohammad. Journal of Artificial Intelligence Research, volume 50, pages 723-762, August 2014. BibTeX
- Meta-Level Sentiment Models for Big Social Data Analysis. F. Bravo-Marquez, M. Mendoza and B. Poblete. Knowledge-Based Systems Volume 69, October 2014, Pages 86–99. BibTex
- Stance and sentiment in tweets. Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Special Section of the ACM Transactions on Internet Technology on Argumentation in Social Media 17(3). BibTeX
- Sentiment strength detection for the social Web. Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Journal of the American Society for Information Science and Technology, 63(1), 163-173. BibTex
Please cite the following paper if using this package in an academic publication:
- Emotion Intensities in Tweets. Saif M. Mohammad and Felipe Bravo-Marquez. In Proceedings of the Joint Conference on Lexical and Computational Semantics (*Sem), August 2017, Vancouver, Canada.
You should also cite the papers describing any of the lexicons or resources you are using with this package.
Here is the BibTex entry for the package along with the entries for the resources included in the package.
Here is the BibTex entry just for the package.
- Email: fbravoma at waikato.ac.nz
- If you have questions about Weka please refer to the Weka mailing list.