V arsini_and_ K irthanna@ D ravidian L ang T ech- ACL 2022-Emotional Analysis in T amil (2022)

Abstract: In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.

## introduction emotion detection is the process of detecting the different human emotions such as anger, disgust, joy, sadness, surprise, love, anticipation, so on and so forth @xcite . "emotion identification", "emotion analysis" and "emotion detection" all mean the same and can be used interchangeably @xcite . people who are users of social media use these platforms as a way to express their feelings, thoughts and opinions on a wide range of topics. these feelings may be positive or neutral or negative. the book "emotions in social psychology", written by w. gerrod parrot, in 2001 @xcite . in which he explained that the human emotion system can be formally classified into an emotion hierarchy with six classes at the primary level, namely surprise, love, anger, fear, sadness and joy, while certain other words fall in the secondary and tertiary levels. emotion detection is used in various fields such as the business world to analyze how people feel about their new product; in the medical world by identifying the way people respond to a pandemic @xcite @xcite @xcite @xcite .emotion identification is also used in monitoring the feelings and emotions of the users who use social platforms such as instagram, facebook, youtube, twitter and many more @xcite . analyzing the emotions felt by the author using the text is quite challenging while also interesting and essential, as most of the time these text messages not only express the emotion directly by using emotional words and emojis but also the interpretation of the meaning of concepts. furthermore, new slang or terminologies or short-forms are being created as each day passes, which make emotion detection from text a more interesting as well as a challenging problem for us to tackle @xcite . tamil language is a dravidian language that is natively spoken by the people of tamil nadu in south asia. it is also the official language of two sovereign nation, sri lanka and singapore as well as the official language of the union terrirtory of puducherry @xcite @xcite @xcite . tamil was known as tamilakam in the time period of the 6th century to the 3rd century ce. tamil is the first indian classical language to listed as classical language, and is one of the world's oldest classical languages that is still spoken. there are 12 vowels, 18 consonants, and one special character, the aytam, in the present tamil script. the vowels and consonants merge to form 216 compound characters, for a total of 247 characters (12 vowels + 18 consonants + 1 aytam + (12 x 18) combinations) @xcite @xcite @xcite . the paper is organized as follows. section 2 discusses the related work on emotion analysis. the data set about the shared task is in section 3.1. section 4 outlines the features of the proposed system. section 5 concludes the paper. ## related work emotional analysis from text is considered as an interesting as well as a challenging task in nlp. however due to lack of data set in tamil language it is difficult to conduct high level research in this area. the tamilemo @xcite introduced a labelled data set that is manually annotated of more than 42,000 tamil youtube comments and is labelled for 31 emotions including neutral. the main aim of the data set is to improve the detection of emotions from tamil texts. they have also created three different groupings of emotions namely 3-class, 7-class and 31-class. actsea @xcite presented a corpus for emotion analysis that is a scalable semi-automatic approach for creating annotated corpus for tamil and sinhala. they gathered data from an online social platform, twitter, and then manually annotated them after cleaning it. they collected 6,00,280 tamil tweets and 3,18,308 sinhala tweets which now make them have one of the largest data sets for the languages tamil and sinhala. in the year 2007, two people -strapparava and mihalcea presented three detailed systems that took part in the semeval 2007 affective text task. the three systems were rule-based, unsupervised and supervised systems. they noted that the rule based system performed the best for 4 emotion classes out of 6, while the supervised system did the best in the remaining two emotion classes. this was done for the language, english. in the year 2007, yang et al. proved that sequence labellers can outperform traditional classifier (support vector system) on a dataset of blogs, increasing the accuracy to 43.35 from 32.88. ## our system methods described in the previous section, i.e., section 3 are modified and integrated to extend their capabilities and to improve the performance for which a simple and easy to understand model is designed shown in figure 2 . ## conclusion in conclusion, the proposed system analyses emotions from text messages that are written in tamil, using a very simple and straightforward method. research in the domain of emotion analysis has flourished significantly over the past few years, making it a need to take a look back at the big picture that these individual works have led to. there are many methods and models to analyse emotions on text. @xcite the dictionary-based approach is quite straightforward and adaptable to 3 167 apply to any language.