Supernova@ D ravidian L ang T ech 2023@Abusive Comment Detection in T amil and T elugu - ( T amil, T amil- E nglish, T elugu- E nglish) (2023)

Abstract: This paper focuses on using Support Vector Machines (SVM) classifiers with TF-IDF feature extraction to classify whether a comment is abusive or not.The paper tries to identify abusive content in regional languages.The dataset analysis presents the distribution of target variables in the Tamil-English, Telugu-English, and Tamil datasets.The methodology section describes the preprocessing steps, including consistency, removal of special characters and emojis, removal of stop words, and stemming of data. Overall, the study contributes to the field of abusive comment detection in Tamil and Telugu languages.

## introduction those statements that harbour ill feelings towards a person or a group of people are categorised as abusive comments. these comments consist of either profanity or racist, sexist, xenophobic, homophobic or transphobic connotations targeting members belonging to certain communities @xcite . identification of abuse over online social networks has proven to be a tedious task due to the overwhelming volume of content generated through social media @xcite . these platforms offer a wide reach and the provision of anonymity can empower individuals to partake in hate speech, as they perceive themselves to be protected from facing immediate consequences for their actions. a large portion of the users on social media platforms engage with other users in their respective native languages and most abusive content detection models are not trained to handle the diversity that exists in these numerous regional languages. the task of identifying abusive comments within the tamil and telugu languages @xcite is notably intricate and complex owing to the lack of linguistically tailored re-sources. this complexity stems from the scarcity of well-annotated datasets @xcite and proficiently trained models specific to these languages. the distinctive linguistic structures and contextual intricacies inherent in tamil and telugu pose obstacles to the creation of precise algorithms for detecting abusive language. abusive comments can lead to a hostile online environment, discouraging users from engaging in discussions or expressing themselves freely. abusive comment detection in these languages allows for targeted content moderation that aligns with the linguistic and cultural context. overall, abusive comment detection in social media texts is essential for promoting user safety and enhancing user experience while fostering inclusivity and sustaining a respectful online environment. ## related work though much work has been done to identify offensive content in major languages such as english @xcite , it is an arduous task to identify and flag offensive and abusive content in low-resource languages, in the scope of our study, dravidian languages, due to scarcity and unavailability of annotated datasets @xcite . due to the predominance of the english script, the datasets involve multiple data points incorporating elements of code-switching or codemixing @xcite @xcite . however @xcite ,attempted to detect the same in urdu and roman urdu using, analysing and comparing five diverse ml models (svm, nb, logistic, ibk and jrip) and four dl models (blstm, clstm, lstm and cnn). it was found that the convolutional neural network outperforms the other models and achieves 96.2 and 91.4 percentage accuracy on urdu and roman urdu. the results also revealed that the one-layer architectures of deep learning models give better results than two-layer architectures. more relevant to our cause, @xcite annotated a bengali corpus of 3000 transliterated bengali comments and found that support vector machine (svm) shows the highest efficacy for identifying abusive content. however, it is important to note that the dataset created was unbiased which may potentially be the cause for the outperformance of svm. the paper delves deeper into the ubiquity of transliterated bengali comments in social media as it renders monolingual approaches futile. it also addresses the issue of the lack of publicly available data for such low-resource languages. other notable contributions allied with the scope of our study include @xcite pre-processing, svm), each providing an extensive analysis of the pre-processing phase and the usage of svm classifiers respectively, highlighting their merits and efficacy. ## dataset analysis the task has been furcated into three subdivisions based on the language of choice, namely tamil-english, tamil and telugu-english. the target variables of the given datasets have been described below. the provided labels for the tamil-english and tamil tasks include none-of-the-above, transphobic, counter-speech, misandry, homophobia, hope-speech, xenophobia and misogyny while the telugu-english includes hate and non-hate labels. the data distribution of each dataset is provided below. figure 2: data distribution of tamil-english figure 3: data distribution of tamil analysis of the data distribution provides insights into the class imbalances which could potentially hinder the performance of the models without the appropriate measures in place. the definition and usage of stop-words is crucial for the effectiveness of such code-mixed and code-switched datasets. while stop-words lists for languages such as english and spanish have been implemented in the nltk.corpus library, the manual creation of stop-words lists for certain low-resource languages was necessitated. this was executed with the usage of previous domain and linguistic knowledge as well as online resources. ## results and analysis the evaluation of the task is done based on the following performance metrics: precision, recall and f1-score. recall measures the classifier's ability to identify positive instances correctly while precision is a measure of how accurate the positive predictions are. figure 5: classification report of logistic regression on telugu dataset figure 6: classification report of linear svc on telugu dataset on exploration of the plethora of prospective models, the weighted average and macro average f1 scores of the models implemented for the telugu dataset were as follows: logistic regression (0.74), svm (0.74) and linear svc (0.73) (figures 6, 7 and 8). svm and logistic regression had similar results, which outperformed linear svc marginally. nonetheless, in comparison to logistic regression, the precision, recall and class-wise performance metrics of svm were superior, corroborating the initial hypothesis on the class imbalances in the dataset. figure 8: classification report of logistic regression on tanglish dataset figure 9: classification report of linear svc on tanglish dataset however, a point of interest is the superiority of linear svc over logistic regression and svm classifiers on the tamil and tamil-english datasets. the classes in linear svc are separable by a linear hyperplane as opposed to svm wherein kernel functions are employed to convert the non linear spaces to linear spaces by transforming data into a higher dimension. hence, the better performance of the svc classifier could be attributed to the linear separability of the tamil and tamil-english datasets since it minimises the probability of inaccurate classifications. furthermore, a deeper analysis of the classification reports substantiated the hypothesis regarding the impact of class imbalances on the model's performance. evident from the aforementioned performance metrics, though the accuracy of the classifiers are similar for all the datasets utilised, the classifiers proved to perform significantly better with regard to the macro and weighted f1-scores on the telugu dataset than the tamil and tamil-english datasets due to the lack of parity in the latter. specifically on analysing the label-wise metrics, a predominant inference is the inability of the svm, logistic regression and linear svc classifiers to generalise on the test data with a smaller quantity of data points for each label. ## conclusion our paper describes the models implemented that detect abusive comments in tamil and telugu. the objective was to classify comments as abusive or non-abusive. data preprocessing was undertaken to ensure uniformity and three models were implemented along with term frequency-inverse document frequency feature extraction. support vector machines (svm) classifiers, logistic regression and linear svc were utilised to build our classification models. the svm model performed the best on the telugu dataset with a macro average and weighted f1-score of 0.74, while the linear svc model proved to perform better on the tamil and tamil-english datasets due to the relatively linear nature of the datasets utilised. this discrepancy is quite apparent particularly with regard to the macro average f1-score. from a rudimentary perspective, the issues in dealing with datasets involving low-resource languages were acknowledged and rectified by appropriate measures such as the creation of stop words lists for such languages. another cardinal drawback of the methods explored is due to the class imbalances. in future works, this could potentially be remedied by implementing clustering methods, bootstrapping or data enrichment techniques.