MUCS @ D ravidian L ang T ech@ ACL 2022: Ensemble of Logistic Regression Penalties to Identify Emotions in T amil Text (2022)

Abstract: Emotion Analysis (EA) is the process of automatically analyzing and categorizing the input text into one of the predefined sets of emotions. In recent years, people have turned to social media to express their emotions, opinions or feelings about news, movies, products, services, and so on. These users’ emotions may help the public, governments, business organizations, film producers, and others in devising strategies, making decisions, and so on. The increasing number of social media users and the increasing amount of user generated text containing emotions on social media demands automated tools for the analysis of such data as handling this data manually is labor intensive and error prone. Further, the characteristics of social media data makes the EA challenging. Most of the EA research works have focused on English language leaving several Indian languages including Tamil unexplored for this task. To address the challenges of EA in Tamil texts, in this paper, we - team MUCS, describe the model submitted to the shared task on Emotion Analysis in Tamil at DravidianLangTech@ACL 2022. Out of the two subtasks in this shared task, our team submitted the model only for Task a. The proposed model comprises of an Ensemble of Logistic Regression (LR) classifiers with three penalties, namely: L1, L2, and Elasticnet. This Ensemble model trained with Term Frequency - Inverse Document Frequency (TF-IDF) of character bigrams and trigrams secured 4th rank in Task a with a macro averaged F1-score of 0.04. The code to reproduce the proposed models is available in github1.

## introduction emotions are a form of psychological state of human mind and in texts the emotions are commonly represented through content bearing words such as happiness, anger, joy, disgust, boredom, depression, etc. the process of automatically analyzing and categorizing the input text into one of the predefined sets of emotions like happy, sad, angry and so on is called emotion analysis @xcite . analyzing the text for emotions helps to improve an existing process, grab new opportunities, capture the real response of audiences for their movies and reality shows, recognize and predict the market trends, and so on @xcite @xcite @xcite . today internet and social media have become a popular platform for users to express the emotions, views, sentiments and opinions. the freedom to users to express their emotions about anything and everything on social media is increasing the social media text containing emotions @xcite . further, the freedom of the use of language on social media makes the analysis of social media text very challenging. the large volume and complexity of social media data makes the analysis of such data very challenging and interesting. most the ea works focus on english language leaving the task in several indian languages including tamil unexplored for the task @xcite . due to the availability of a large volume of user-generated social media data in tamil containing different emotions, ea in tamil is gaining popularity @xcite . in recent years, there has been an increase in the ea of text in classical languages like tamil the growing number of tamil users on social media platforms and the increasing number of posts and comments shared by these users are making it nearly impossible to track and control the content manually @xcite @xcite @xcite . hence, there is a need for tools or models to analyse the emotions in the social media comments automatically. ea is an open-ended issue because of the creative users' cre-ative posts on social media @xcite . to address the challenges of ea in tamil, in this paper, we -team mucs, describe the model submitted to "emotion analysis in tamil" foot_1 shared task organized by dravidianlangtech@acl 2022. this task aims to classify the input comment in tamil into one of eleven emotion categories. the proposed methodology consists of an ensemble of lr classifiers with different regularizations or penalties, namely: lasso (l1) regularization, ridge (l2) regularization and elasticnet regularization. tf-idf of character bigrams and trigrams is used to train the lr classifiers and soft voting is used to classify the input comment into one of eleven categories. the following is a breakdown of the paper's structure. section 2 contains the literature review and section 3 explains the proposed methodology. section 4 describes the experiments conducted to identify and determine type of emotions, as well as the outcomes and the paper concludes in section 5 with future work. ## literature review researchers are trying to develop tools for processing the tamil language for various applications such as ea, text summarization, sentiment analysis (sa) and so on @xcite . @xcite analyzed the performance of sa and emotion recognition using bidirectional encoder representations from transformers (bert) models on real-world twitter dataset. the experimental results showed that the models scored 0.92 and 0.90 accuracies for sa and emotion recognition, respectively. vasantharajan et al. (2022) developed the largest manually annotated dataset of over 42k tamil youtube comments and categorized them into 31 emotions in order to recognize emotional statements. they established three distinct groups of emotions that are of 3-class, 7class, and 31-classes. for the 3-class group dataset, they used multilingual representations for indian languages (muril) pre-trained model trained on english and 16 indian languages and obtained a macro average f1-score of 0.60. for 7-class and 31-class groups, the random forest (rf) model performed well with macro average f1-scores of 0.42 and 0.29, respectively. to determine the category of emotions, alotaibi (2019) trained support vector machine (svm), k-nearest neighbors (knn), and lr classifiers on the international survey on emotion antecedents and reactions (isear) dataset using tf-idf features. lr classifier obtained 0.86 and 0.85 as precision and f1-score respectively. using the benefits of convolutional neural network (cnn) and bidirectional long short term memory (bilstm), @xcite proposed an attention-based c-bilstm model to classify emotional states of poetry texts into different emotional states like love, joy, hope, sadness, anger, etc. experimental results showed an accuracy of 88% for their model. even though several techniques have been developed to detect emotions in the text, very few attempts have been made for the tamil language. this opens up lots of possibilities to conduct experiments on ea of tamil texts including social media data. ## methodology inspired by @xcite and @xcite an ensemble of lr classifiers is proposed to identify the emotions in tamil text and classify them into one of the given eleven categories and the framework of the proposed model is shown in figure 1 . the proposed model consists of three modules, namely: pre-processing, feature extraction and classifier construction which are described briefly below: ## experiments and results statistics of the dataset for task a in the ea shared task is summarized in with that of the proposed model in terms of macroaveraged f1-score is shown in figure 2 . the proposed model obtained macro-averaged f1-scores of 0.20 and 0.04 for dev set and test set respectively. it is clear that the scores for dev set and test set were low because of imbalanced nature of the dataset. class distribution has a significant impact on the predictions and the same is reflected in the results. 'neutral' class has the maximum distribution of 34.07% of the overall distribution, whereas 'fear' class has a least distribution of 0.70%. the proposed model exhibited a low f1-score because of the large difference between the number of samples in the classes.