IRNLP _ DAIICT @ D ravidian L ang T ech- EACL 2021:Offensive Language identification in D ravidian Languages using TF - IDF Char N-grams and M u RIL (2021)

Abstract: This paper presents the participation of the IRNLPDAIICT team from Information Retrieval and Natural Language Processing lab at DA-IICT, India in DravidianLangTech-EACL2021 Offensive Language identification in Dravidian Languages. The aim of this shared task is to identify Offensive Language from a code-mixed data-set of YouTube comments. The task is to classify comments into Not Offensive (NO), Offensive Untargetede(OU), Offensive Targeted Individual (OTI), Offensive Targeted Group (OTG), Offensive Targeted Others (OTO), Other Language (OL) for three Dravidian languages: Kannada, Malayalam and Tamil. We use TF-IDF character n-grams and pretrained MuRIL embeddings for text representation and Logistic Regression and Linear SVM for classification. Our best approach achieved Ninth, Third and Eighth with weighted F1 score of 0.64, 0.95 and 0.71in Kannada, Malayalam and Tamil on test dataset respectively.

## introduction the emergence of smartphones and widespread access to internet, social media platforms like facebook, twitter, youtube, etc have become increasingly popular. people express their opinions on such platforms on various issues. due to lack of moderation, a significant amount of offensive content is often posted on these platforms. since social media is become a indispensable part of our life, the content posted on the social media platforms has great impact on the society. it has been seen recently that offensive and provoking content on these platforms can lead to riots. hence the moderation of content in these platforms is a very important task. since the amount of content generated is very high, there is a need to perform automated moderation of the content. natural language processing can used to perform automated analysis of text. by identifying and classifying the text content posted, offensive content can be removed and users could be warned. this can help in curbing offensive content online. the goal of this shared task is to classify offensive language from a code-mixed data-set of comments of dravidian languages collected from youtube. shared task was introduced as six class classification of the youtube for three dravidian languages: kannada, malayalam and tamil @xcite our approaches use tf-idf character n-grams and muril embeddings for the text representation and logistic regression, linear svm, randam forest for classification. the rest of this paper is organized as follows. in section we discuss the related work followed by section 3 which describes the shared task dataset and methods are presented in section 4. results and analysis is given in final section 5 and section 6 present conclusion. ## related work the identification of hate and offensive speech in social media is of great importance and receives much attention in the text classification community. due to the lack of resources and morphological complexity there is a huge demand for research in code-mixed dravidian languages. some of the shared tasks in the recent past are offenseval @xcite , @xcite and hasoc @xcite . among these shared tasks, hasoc 2020 shared task is based on dravidian languages. in offenseval @xcite , bert @xcite , roberta @xcite 4 methods for all the youtube comments we first preprocess the text and then create a text representation and finally classify the text using the machine learning classifiers. figure 1 illustrates the set of steps used to classify the youtube comments. ## results and analysis the submission evaluation on the test data of all the three languages is shown in table tables 2, 3, 4. our methods achieve ninth, third and eighth rank in kannada, malayalam and tamil respectively. for the kannada language task, tf-idf (char) + lr and tf-idf (char) + svm achieves the best results with weighted f1 score of 0.65 but for ranking task organize consider tf-idf(char) + rf with weighted f1 score of 0.64. for the malayalam language task, muril representation of the text with random forest classifier (muril + rf) achieves the best weighted f1 score of 0.95. for tamil language tf-idf character n-grams representation of the text with logistic regression classifier ( tf-idf (char) + lr) achieves the best results with weighted f1 score of 0.71. ## conclusion in this paper the details regarding our submission in offensive language identification shared task have been presented. we explored two text representation techniques: tf-idf character n-grams and muril and conclude that muril works better for malayalam while tf-idf works better for other two languages. in future to mitigate the data imbalance problem text augmentation techniques like back translation could be applied to oversample the minority class. also, other methods like cnn, rnn and fine tuning of language models could be explored.