O ptimize_ P rime@ D ravidian L ang T ech- ACL 2022: Emotion Analysis in T amil (2022)

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.

## introduction due to the rise in social media, internet users can voice their opinion on various subjects. social networking platforms have grown in popularity and are used for a variety of activities such as product promotion, news sharing, and accomplishment sharing, among others @xcite . emotion analysis or opinion mining is the study of extracting people's sentiment about a particular topic, person, or organization from textual data. emotion analysis has many modern-day use-cases in e-commerce, social media monitoring, market research, etc. tamil is the 18th most spoken language globally (wikipedia contributors, 2022), with over 75 million speakers. developing an approach for emotion analysis of tamil text will benefit many people and businesses. emotion analysis, at its core, is a text classification problem. to date, various approaches have * first author, equal contribution † second author, equal contribution been developed for text classification. earlier, classification models like logistic regression, linear svc, etc., were used. rnn based approaches like lstms also gained much traction because they produced better results than standard machine learning models. the introduction of transformers @xcite changed the course of text classification due to their consistent performance. multiple variations of the transformer have been developed like bert @xcite , albert @xcite , xlm-roberta @xcite , muril @xcite , etc. in this paper, we have tried various approaches to detect emotions from social media comments. we have used three distinct ways to get optimal results: ensemble models, recurrent neural networks (rnns), and transformer-based approaches. this paper will contribute towards future research in emotion analysis in low-resource indic languages. ## related work emotion analysis has recently gained popularity, as large volumes of data are added to social networking sites daily. earlier studies focus more on lexicon-based approaches, and they make use of a pre-prepared sentiment lexicon to classify the text. e.g., in @xcite , @xcite and yan @xcite , lexicon-based approaches are used; however, if unrelated words express emotions, this approach fails. to overcome the limitations of lexical/keywordbased approaches, learning-based approaches were introduced. in this, the model learns from the data and tries to find a relationship between input text and the corresponding emotion. researchers have tried out both supervised and unsupervised learning approaches. e.g., in @xcite , tweet classification was performed using naïve bayes (supervised learning). a combination of lexicon-based and learningbased approaches were used to perform classification on a multilingual dataset in @xcite . transfer learning-based approaches work well for low-resource languages. transfer learning allows us to reuse the existing pre-trained models. for example, @xcite used a transfer learning approach to classify text in hindi. lately, transformer-based models have been consistently outperforming other architectures, including rnns. the development of models like muril @xcite , xlm-roberta @xcite , indic bert @xcite , and m-bert @xcite has encouraged research in various low resource as well as high resource languages. ## dataset description the shared task on emotion analysis in tamil-acl 2022 aims to classify social media comments into categories of emotions. the emotion analysis in tamil dataset @xcite consists of two datasets. the first dataset is for task a and has 11 categories of emotions which are: neutral, joy, ambiguous, trust, disgust, anger, anticipation, sadness, love, surprise, fear. while the second is for task b and has 31 more specific categories of emotions. the distribution of data among classes is given in the train, dev, and test datasets have 14,208, 3,552, and 4,440 data points, respectively. each data point in the training data has the text in tamil and its corresponding label in english. ## methodology to classify social media comments into different emotions, we used three different approaches: ensemble models, recurrent neural networks, and transformers. figure 1 . shows the architecture of all the three approaches foot_0 . ## results the results obtained for task a and task b are given in ## conclusion the aim of this paper was to classify social media comments. we used three approaches: ensemble models, recurrent neural networks (rnns), and transformers. out of these models, for task a, xlm-roberta outperformed all other models with a macro-averaged f1 score of 0.27. however, in task b, muril outperformed all other models with a macro averaged f1 score of 0.125. overall, it is observed that the models classify emotions like joy, sadness, neutral, and sentences having ambiguity well. however, the models classify more complex emotions like anger, fear, and sadness with much less accuracy. in the future, various techniques like genetic algorithm-based ensembling can be tried to improve the performance of the models.