COMBATANT @ T amil NLP - ACL 2022: Fine-grained Categorization of Abusive Comments using Logistic Regression (2022)

Abstract: With the widespread usage of social media and effortless internet access, millions of posts and comments are generated every minute. Unfortunately, with this substantial rise, the usage of abusive language has increased significantly in these mediums. This proliferation leads to many hazards such as cyber-bullying, vulgarity, online harassment and abuse. Therefore, it becomes a crucial issue to detect and mitigate the usage of abusive language. This work presents our system developed as part of the shared task to detect the abusive language in Tamil. We employed three machine learning (LR, DT, SVM), two deep learning (CNN+BiLSTM, CNN+BiLSTM with FastText) and a transformer-based model (Indic-BERT). The experimental results show that Logistic regression (LR) and CNN+BiLSTM models outperformed the others. Both Logistic Regression (LR) and CNN+BiLSTM with FastText achieved the weighted F_1 -score of 0.39. However, LR obtained a higher recall value (0.44) than CNN+BiLSTM (0.36). This leads us to stand the 2^{nd} rank in the shared task competition.

## introduction with the rapid growth of user-generated content in social media, the emergence of abusive content also increased dramatically @xcite . this insurgence has become a reason of worry for governments, policymakers, social scientists and tech companies since it has detrimental consequences on society @xcite . currently, we are living in an information era where social media plays a vital role in shaping people's minds, and opinions @xcite . therefore mitigating the usage of abusive language has become extremely important @xcite . companies like facebook, youtube, twitter have been trying to achieve this for years @xcite . it is impossible to monitor and moderate social media content manually because of its large volume and its messy forms @xcite . therefore, it is necessary to develop an intelligent system to tackle this issue. several studies have been conducted to detect abusive language for english, and other high resource languages @xcite @xcite @xcite . in contrast, a low-resource language like tamil remained out of focus and has much room for improvement @xcite . tamil is an official language of tamil nadu, sri lanka, singapore, and the union territory of puducherry in india @xcite . significant minority speak tamil in the four other south indian states of kerala, karnataka, andhra pradesh, and telangana, as well as the union territory of the andaman and nicobar islands @xcite @xcite . tamil, as a dravidian language, descended from proto-dravidian, a proto-language, according to bhadriraju krishnamurti. linguistic reconstruction implies that proto-dravidian was spoken about the third millennium bc, likely in the peninsular indian region surrounding the lower godavari river basin. the material evidence implies that the speakers of proto-dravidian belonged to the civilization linked with south india's neolithic complexes. the earliest old tamil documents are small inscriptions in adichanallur dating from 905 bc to 696 bc. tamil has the most ancient non-sanskritic indian literature @xcite @xcite @xcite . this work aims to build a system that can classify abusive language from tamil text concerning eight different categories: hope-speech, homophobia, misandry, counter-speech, misogyny, xenophobia, transphobia and none-of-the-above. various machine learning (ml), deep learning (dl), and transformer-based models have been used to attain this goal. the key contributions of this work are illustrated in the following: ## related work recently, researchers are trying to develop methods and tools to analyze social media sites like twitter, facebook, and snapchat since these mediums has become integral part of our life @xcite . studies have already been conducted to detect abusive or offensive comments on social media @xcite @xcite . few researches has focused on other overlapping domains such as hate speech @xcite ## methodology the techniques and methods used to detect abusive categories for given youtube comments are briefly explained in this section. we cleaned the raw data first by stripping away noisy elements and then extracted features @xcite using various feature extraction techniques, including tf-idf, word2vec, and fasttext. we used ml and dl based techniques for the baseline evaluation. the schematic process of our approach is depicted in figure 1 . ## results and analysis the performance of the various methods on the test set is reported in it is observed that, among the ml models, the lr model outperformed the dt and svm models achieving the highest macro f 1 -score (0.39). word2vec features. surprisingly, the performance is improved to 0.39 when we used a pre-trained word embedding (i.e. fasttext). unfortunately, the transformer model, indic-bert, could not provide satisfactory performance on the test set. moreover, we conducted a thorough investigation into all of the employed models. the outcomes of the investigation is presented in ## conclusion this paper presents the various models developed to classify abusive comments in tamil. this work used three ml, two dl classifiers and one transformer-based model to perform the classification task. the lr model with tf-idf features outperformed all models by obtaining the highest macro f 1 -score (0.39). although the combined cnn and bilstm model (c+b) achieved a similar macro f 1 -score (0.39) with fasttext features, the lr model obtained a higher recall value (0.44). surprisingly, indic-bert performed poorly compared to the ml and dl models. these inferior results might occur because of the prevalence of local words, which is unknown to the model. it will be interesting to investigate how the model performs if the dataset is used in more advanced transformer models (xml-r, electra, mbert, muril). furthermore, we aim to tackle the data imbalance problem by adding more diverse data to the existing corpus that might improve the model's performance.