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.
翻译:本文旨在对泰米尔语的社交媒体评论进行情感分析。 情感分析是确定文本情感背景的过程。 在本文中,我们介绍了AFL 2022年“泰米尔人情感分析”共同任务AFIMITION-Prime团队获得的调查结果。 任务旨在将社交媒体评论分为情感类别,如Joy、 Anger、 Trust、 Disgust等。 任务还进一步分为两个子任务, 一个分11种情绪,另一个分31种情绪。 我们采取了三种不同的办法来解决这一问题: 变压模型、常态神经网络(RNN)和Ensemble模型。 XLM-ROBERTA在第一项任务中表现最佳,宏观平均F1分为0.27分,而MuRIL提供了第二项任务的最佳结果,宏观平均F1分为0.13分。