Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil
This work addresses emotion classification for Tamil language users, but it is incremental as it applies existing methods to a new dataset.
This paper tackled emotion analysis in Tamil social media comments by participating in an ACL 2022 shared task, achieving a macro-averaged f1 score of 0.27 for 11 emotion categories and 0.13 for 31 categories using transformer-based models.
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.