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.

本文旨在对泰米尔社交媒体评论进行情感分析，包括11个和31个特定类别的情感，通过使用转换器模型、循环神经网络和集成模型三种方法解决该问题，其中XLM-RoBERTa在第一个任务中表现最佳，宏平均f1得分为0.27，而MuRIL在第二个任务中提供了最佳结果，宏平均f1得分为0.13。

Optimize_Prime@DravidianLangTech-ACL2022: 泰米尔语情感分析