J udith J eyafreeda A ndrew@ T amil NLP - ACL 2022: CNN for Emotion Analysis in T amil (2022)

Abstract: Using technology for analysis of human emotion is a relatively nascent research area. There are several types of data where emotion recognition can be employed, such as - text, images, audio and video. In this paper, the focus is on emotion recognition in text data. Emotion recognition in text can be performed from both written comments and from conversations. In this paper, the dataset used for emotion recognition is a list of comments. While extensive research is being performed in this area, the language of the text plays a very important role. In this work, the focus is on the Dravidian language of Tamil. The language and its script demands an extensive pre-processing. The paper contributes to this by adapting various pre-processing methods to the Dravidian Language of Tamil. A CNN method has been adopted for the task at hand. The proposed method has achieved a comparable result.

## introduction emotion analysis is a task of classification of emotions in text. there are several application for this task such as reviews analysis in e-commerce, public opinion analysis, extensive search, personalized recommendation, healthcare, and online teaching @xcite @xcite @xcite . a lot of research has been done on classifying comments, opinions, movie/product reviews, ratings, recommendations and other forms of online expression into positive or negative sentiments @xcite @xcite @xcite . though there have been several research works around emotion recognition in english language, there are not many in dravidian languages @xcite @xcite . the four major dravidian languages are tamil, telugu, malayalam and kannada. this paper explores the idea of using deep neural networks specifically cnn for the purpose of emotion recognition in text from the dravidian language of tamil @xcite . tamil is one of the world's longest-surviving classical languages @xcite @xcite . according to a. k. ramanujan, it is "the only language of modern india that is recognizably continuous with a classical history." because of the range and quality of ancient tamil literature, it has been referred to as "one of the world's major classical traditions and literatures." for about 2600 years, there has been a recorded tamil literature @xcite @xcite . the earliest period of tamil literature, known as sangam literature, is said to have lasted from from 600 bc to ad 300. among dravidian languages, it possesses the oldest existing literature. the earliest epigraphic documents discovered on rock edicts and "hero stones" date from the 6th century bc @xcite @xcite . the task in @xcite ) is categorized in two subtasks, both of which dealing with a corpus in the dravidian language of tamil. the first one aims at classifying social media comments in 8-10 classes where the classes are in english. the second subtask involves classifying text into one of the 30 classes, where the classes are also in tamil. the classification systems performance has been measured in terms of macro averaged precision, macro averaged recall and macro averaged f-score across all the classes. ## related work with the increase in social media content in the recent past, a lot of focus has been given to emotion analysis. several machine learning and deep learning approaches have been developed for this cause. @xcite proposed a manual corpus annotation for emotions and sentiments in news articles. @xcite describes an experiment for automatic identification of six different emotions in text including anger, disgust, fear, joy, sadness and surprise. the authors propose both knowledge based and corpus based methods for this purpose. @xcite uses emotion detection to predict the future stock returns by applying a emotion classifier to tweets from the 2016 presidential election and financial tweets. @xcite uses a supervised model.the model developed is a hybrid one consisting of two completely different approaches. the first approach uses emotion-words set and several textual features to classify and score text according to the emotions. the second approach uses standard classifiers like smo and j48 to classify tweets. finally, these approaches are combined to detect emotions in text more effectively. @xcite uses convolutional neural network architecture for emotion identification in twitter messages. the model has been applied on twitter messages for emotion identification related to public local services. this is an unsupervised method. (savigny and purwarianti, 2017) compared many methods for using word embedding in a classification task, namely average word vector, average word vector with tf-idf, paragraph vector, and by using convolutional neural network (cnn) algorithm. the authors showed that the accuracy of the classification increases while word embeddings are used in combination with cnn. @xcite addresses the problem where a sentence can evoke more than one emotion. for this purpose, the authors introduce an emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. ## data the datset for the two subtasks are from @xcite . ## pre-processing the tamil text needs some pre-processing before training a deep learning algorithm. the preprocessing techniques are similar to ones in (andrew, 2021). ## results the performance of the classification system has been evaluated in terms of macro averaged precision, macro averaged recall and macro averaged f-score across all the classes. the evaluation has been performed with the sklearn package on python @xcite . the results for both tasks a and b are shown in a precision of 0.150, a recall of 0.122 and a f1score of 0.094 is achieved for task a. the highest scores of metrics achieved for task a are precision is 0.220, recall is 0.250 and f1 score is 0.210. a precision of 0.094, a recall of 0.068 and a f1score of 0.054 is achieved for task b. the highest scores of metrics achieved for task b are precision is 0.15, recall is 0.171 and f1 score is 0.151. in general this is quite low. it has to be kept in mind that task 2 had both the text and labels in the dravidian language of tamil. it can be noted that when the language of the labels/category is in english, the results are better than when both the labels/category is in tamil. @xcite shows that pre-processing dravidian texts help improve the results when used with machine learning models, however, this does not seem to be the case with deep learning techniques. this is because deep learning techniques requires huge amount of training data. for a language like tamil, such models are not easily available due to the lack if data. using language models such as bert trained for the dravidian language of tamil over a large corpus could help in more accurate classification of emotions. there is clearly a huge amount of efforts that needs to go in encoding and decoding of dravidian language scripts. translating dravidian language scripts to latin alphabets might not be the best approach for emotion classification. this is a critical point of pre-processing that needs to be considered in future works. any new model built should be able to process the text with the script of the dravidian language itself.