EmpNa at WASSA 2021: A Lightweight Model for the Prediction of Empathy, Distress and Emotions from Reactions to News Stories

Giuseppe Vettigli, Antonio Sorgente


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:
Bibkey:
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)
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