论文标题
Optimize_prime@dravidianlangtech-acl2022：泰米尔语中的情感分析
Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil
论文作者
论文摘要
本文旨在对泰米尔语中的社交媒体评论进行情感分析。情绪分析是识别文本的情感背景的过程。在本文中，我们介绍了团队Optimize_prime在ACL 2022共享任务“泰米尔语中的情感分析”中获得的发现。该任务旨在将社交媒体评论分为喜悦，愤怒，信任，厌恶等情感类别。该任务进一步分为两个子任务，一个具有11种广泛的情感类别，另一个具有31个特定类别的情感。我们实施了三种不同的方法来解决此问题：基于变压器的模型，经过的神经网络（RNN）和集合模型。 XLM-Roberta在第一个任务上表现最好，宏观平均的F1得分为0.27，而Muril在第二任任务上以0.13的宏观平均F1得分提供了最佳的结果。
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.
