Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil

TL;DR

This paper presents approaches for emotion analysis in Tamil social media comments, utilizing transformer-based models, RNNs, and ensemble methods, with XLM-RoBERTa and MuRIL achieving the best results in a shared task.

Contribution

The paper introduces multiple models for Tamil emotion classification and reports the first results on this specific shared task, highlighting transformer models' effectiveness.

Findings

XLM-RoBERTa achieved 0.27 F1 score on broad emotion categories.

MuRIL achieved 0.13 F1 score on specific emotion categories.

Transformer models outperformed other approaches in this task.

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.