Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil

Omkar Gokhale, Shantanu Patankar, Onkar Litake, Aditya Mandke, Dipali Kadam

Abstract
This paper aims to perform an emotion analysis of social media comments in Tamil. Emotion analysis is the process of identifying the emotional context of the text. In this paper, we present the findings obtained by Team Optimize_Prime in the ACL 2022 shared task “Emotion Analysis in Tamil.” The task aimed to classify social media comments into categories of emotion like Joy, Anger, Trust, Disgust, etc. The task was further divided into two subtasks, one with 11 broad categories of emotions and the other with 31 specific categories of emotion. We implemented three different approaches to tackle this problem: transformer-based models, Recurrent Neural Networks (RNNs), and Ensemble models. XLM-RoBERTa performed the best on the first task with a macro-averaged f1 score of 0.27, while MuRIL provided the best results on the second task with a macro-averaged f1 score of 0.13.
Anthology ID:
2022.dravidianlangtech-1.35
Volume:
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
DravidianLangTech
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
229–234
Language:
URL:
https://aclanthology.org/2022.dravidianlangtech-1.35
DOI:
10.18653/v1/2022.dravidianlangtech-1.35
Bibkey:
Cite (ACL):
Omkar Gokhale, Shantanu Patankar, Onkar Litake, Aditya Mandke, and Dipali Kadam. 2022. Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 229–234, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil (Gokhale et al., DravidianLangTech 2022)
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