PANDAS @ T amil NLP - ACL 2022: Emotion Analysis in T amil Text using Language Agnostic Embeddings (2022)

Abstract: As the world around us continues to become increasingly digital, it has been acknowledged that there is a growing need for emotion analysis of social media content. The task of identifying the emotion in a given text has many practical applications ranging from screening public health to business and management. In this paper, we propose a language-agnostic model that focuses on emotion analysis in Tamil text. Our experiments yielded an F1-score of 0.010.

## introduction over the past decade, advances in technology have progressed at an extraordinary rate, which in turn has rapidly transformed our methods of communication, expedited by the need for all institutions and establishments to 'go digital'. people have adapted to online modes of communication such as social networking sites and online discussion forums. these platforms have many advantages such as the ability to bring people with similar passions together and enable them to exchange their views, or the capacity to allow people to rally together for a common cause. it would be useful for these platforms to identify people with common interests by filtering through their comments. on the other hand, there are also some disadvantages that arise from the abuse of these tools, which are not limited to, but include, the posting of inappropriate or hurtful comments (o' @xcite , @xcite . to ensure moderation in content, it is necessary to monitor posts and comments published on various social media @xcite . research suggests that monitoring social media can be useful in surveying, understanding and predicting public health @xcite , @xcite , @xcite . social media analytics can also aid in enterprise management (lee, 2018) and national security @xcite . a commonly used method to monitor social media is emotion analysis. emotions are subjective mental states that are brought about as reactions to our thoughts or memories, or as reactions to external events in our surroundings. speech and text are both generally associated with an emotion of some kind -joy, sorrow, fear, anger, etc. classifying a given text into one of the many categories of emotions is a constructive way to analyze and obtain some understanding of the text @xcite . such textual emotion analysis has many practical applications. for example, unilever analyzes the emotional expressions of prospective candidates for its entry-level jobs, which helps in saving a significant amount of time during the candidate screening process. the task of emotion analysis in tamil -dravid-ianlangtech@acl 2022 foot_0 @xcite aims to classify a set of given comments by predicting the probable emotions associated with each one. a particular trial faced here is that of the dataset being in the tamil language, for which comparatively less resources are available. in this paper, we have used a language-agnostic sentence embedder called labse, a popular multilingual bert embedding model to identify the emotions in tamil text. the rest of the paper is organized as follows: section 2 outlines any related works ascertained by a literature survey; section 3 provides a description of the dataset; section 4 details the methodology used for this task; section 5 discusses the results and in section 6, a conclusion is put forward. ## related work the field of emotion analysis allows for a multitude of approaches to be used, some of which have been documented in prominent literature. with the recent research and development in speech to text concepts, various researchers have gone about building and testing fast and accurate models for anand kumar madasamy, soman kotti padannayil @xcite formulated a tri-layer rnn for the sail task of classifying tweets in indian languages with 1500 lines of tamil, hindi and bengali, which was applied on the indian textual tweets. this was compared with a naive bayes model given by the sail task, which gave a significantly lesser accuracy. sajeetha thavareesan and sinnathamby mahesan @xcite modeled 5 different approaches for sentiment analysis of tamil texts. they experimented on the uj_corpus_opinions and sail-2015 corpus .they extracted the features using tf, bow , tf-idf , word2vec and fasttext. they subsequently used lexicon based and ml based approaches (using svm , extreme gradient boost egb, random forest rf, neural network nn, linear regression lr, k nearest neighbours knn). they observed that feature extraction using fasttext and egb outperformed the rest. xiaotian lin et al @xcite , performed a multilingual text classification -classification into positive, negative ,neutral and mixed emotions using a plain labse model, which was comparatively better than the models xlm, xlm roberta and multilingual bert. they used the mlm strategy to achieve the desired result. a research work done by niveditha et al @xcite ) modeled an unsupervised approach on the 2015 sail dataset to feature extraction using sentiwordnet and word2vec embeddings. kamal et al. @xcite participated in the sail shared task, which included classifying tweets given in the hindi and bengali languages. they processed the tweets with the emoticons and used multinomial naive bayes model. for opinion mining in hindi (into positive, negative, neutral), they used pos tagging in which adjectives were analyzed to perform the task of mining. other state of the art models include cnn, rnn, bilstm models and ml techniques implemented on these embeddings. the conclusion from the aforementioned literature is that labse shows encouraging results in feature extraction, and such datasets comprising tamil and other subcontinental languages are best served by this transformer. emotional analysis and classification of emotion is found to be done most effectively by svm classifier. in summation, a model which incorporates elements of svm and labse can be expected to be a good approach for this acl task, and it is also a novel outlook, which has not been examined by the authors listed above. ## dataset the dataset under consideration for the task consists of comments made by youtube users in the figure 2: data distribution of the development dataset tamil language. one of the dravidian languages and predominantly spoken in tamil nadu (india), tamil has a unique script and an alphabet that is made up of 12 vowels and 18 consonants that when combined in various ways, can give rise to about 216 compound characters. the comments in this dataset are grouped under 11 different categories based on the emotion that each conveys, as is shown in ## methodology the proposed methodology for this task includes extracting structural features from the processed data and applying classifier models to them. a schematic diagram illustrating the procedure is given in figure 3 . ## results and analysis it is apparent that the results are slightly better when smote is used along with the classification model, although the dataset seems to be so highly unbalanced that there is very little difference between the two results. ## conclusion in this research paper, we have presented a multilingual transformer model for the emotion analysis of tamil text as required by the dravidiantech-lang acl 2022 shared task. labse, a pre-trained language agnostic bert model, was found to perform comparatively well on the tamil dataset. this model yielded an f1-score of 0.010 on the given dataset. we believe that these results can be improved upon highly by using custom embeddings, based on statistical analysis of the language, to process the data before training the model.