Abstract
This paper describes our submission for the WASSA 2021 shared task regarding the prediction of empathy, distress and emotions from news stories. The solution is based on combining the frequency of words, lexicon-based information, demographics of the annotators and personality of the annotators into a linear model. The prediction of empathy and distress is performed using Linear Regression while the prediction of emotions is performed using Logistic Regression. Both tasks are performed using the same features. Our models rank 4th for the prediction of emotions and 2nd for the prediction of empathy and distress. These results are particularly interesting when considered that the computational requirements of the solution are minimal.- Anthology ID:
- 2021.wassa-1.28
- Volume:
- Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Orphee De Clercq, Alexandra Balahur, Joao Sedoc, Valentin Barriere, Shabnam Tafreshi, Sven Buechel, Veronique Hoste
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 264–268
- Language:
- URL:
- https://aclanthology.org/2021.wassa-1.28
- DOI:
- Cite (ACL):
- Giuseppe Vettigli and Antonio Sorgente. 2021. EmpNa at WASSA 2021: A Lightweight Model for the Prediction of Empathy, Distress and Emotions from Reactions to News Stories. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 264–268, Online. Association for Computational Linguistics.
- Cite (Informal):
- EmpNa at WASSA 2021: A Lightweight Model for the Prediction of Empathy, Distress and Emotions from Reactions to News Stories (Vettigli & Sorgente, WASSA 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.wassa-1.28.pdf