CEN - T amil@ D ravidian L ang T ech- ACL 2022: Abusive Comment detection in T amil using TF - IDF and Random Kitchen Sink Algorithm (2022)

Abstract: This paper describes the approach of team CEN-Tamil used for abusive comment detection in Tamil. This task aims to identify whether a given comment contains abusive comments. We used TF-IDF with char-wb analyzers with Random Kitchen Sink (RKS) algorithm to create feature vectors and the Support Vector Machine (SVM) classifier with polynomial kernel for classification. We used this method for both Tamil and Tamil-English datasets and secured first place with an f1-score of 0.32 and seventh place with an f1-score of 0.25, respectively. The code for our approach is shared in the GitHub repository.

## introduction abusive speech refers to any form of communication done with the intention to humiliate, or spread hatred against a vulnerable individual or a vulnerable group on the basis of gender, race, religion, ethnicity, skin color or disability using abusive or vulgar words. it causes psychological effects on the targeted individual and leading them towards unrightful act. in recent years, there has been significant growth in the volume of digital content exchanged by people through social media. online social networks have grown in importance, becoming a source for acquiring news, information, and entertainment. despite the apparent advantages of using online social networks, there is an ever-increasing number of malevolent actors who use social media to harm others. the goal of the shared task is to identify abusive comments in tamil and codemixed tamil-english data developed by collecting youtube comments. the code-mix tamil-english dataset consists of eight different classes 1 https://github.com/prasanth-s-n/ cen-tamil_abusive_comment_detection namely, 'counter-speech', 'homophobia', 'hope-speech', 'misandry','misogyny', 'none-of-theabove', 'transphobic', 'xenophobia'. in addition to the aforementioned eight classes, the tamil dataset consists of one more class, 'not-tamil'. we used random kitchen sink (rks) @xcite algorithms with character word-bound based term frequency-inverse document frequency (tf-idf) @xcite for text representation and classification was performed using support vector machines (svm) classifier @xcite , @xcite . the rest of the paper is organised as follows: section 2 describes about the related works, section 3 describes about the datasets, section 4 describes about the preprocessing and different methods used, section 5 describes about the result and analysis and section 6 concludes the paper. ## related works analysis of online social networks' content is an active research area with tasks like offensive language identification and hope speech detection. recent work in hope speech detection in dravidan languages includes the shared task on hope speech detection in lt-edi in eacl @xcite . abusive language detection for other languages has been done in literature @xcite but as far as we know, this is the first shared task on abusive detection in tamil at this fine-grained level. we used tf-idf because it helps in understanding the importance of a word in the corpus @xcite and we used random kitchen sink (rks) on top of it because rks helps in mapping the data from the feature space to a higher dimensional space @xcite . we used svm because of its ability to perform well in the higher dimensional data @xcite . ## dataset the organisers of the abusive comment detection shared task provided two datasets, where one contains tamil comments and the another one contains code-mixed tamil-english comments @xcite . ## methods we started with preprocessing the youtube comments in the datasets, and the preprocessed texts were converted into vectors. the classification of the youtube comments was carried out by supplying the text vectors to a classifier, svm. figure 1 shows the pipeline of the methodology we followed for this task. ## result and analysis we experimented with four different machine learning classification models. all the four models initially uses tf-idf with char-wb analyzer and max features being 2000 and svm classifier with polynomial kernel and regularization parameter being 100. model-1 uses only svm and tf-idf. model-2 additionally uses smote oversampling technique. this paper briefs the submission of team cen-tamil to the shared task at acl 2022 on abusive comments detection in tamil. we experimented character word bound n-grams based tf-idf with and without rks. the max features for the tf-idf is taken to be 2000. the highly class imbalance problem was solved using smote. we also reported the results obtained without using smote. the svm classifier with polynomial kernel and 100 as regularization with tf-idf, rks and smote gave high macro f1-score of 0.32 for tamil and 0.25 for tamil-english which secured first and seventh place in the shared task respectively. we have not explored transformers based approaches for abusive comment detection. as future works we like to experiment with different transformers like bert and labse along with deep learning architecture like lstm and cnn to improve the results.