CSSCUTN @ D ravidian L ang T ech:Abusive comments Detection in T amil and T elugu (2023)

Abstract: Code-mixing is a word or phrase-level act of interchanging two or more languages during a conversation or in written text within a sentence. This phenomenon is widespread on social media platforms, and understanding the underlying abusive comments in a code-mixed sentence is a complex challenge. We present our system in our submission for the DravidianLangTech Shared Task on Abusive Comment Detection in Tamil and Telugu. Our approach involves building a multiclass abusive detection model that recognizes 8 different labels. The provided samples are code-mixed Tamil-English text, where Tamil is represented in romanised form. We focused on the Multiclass classification subtask, and we leveraged Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Our method exhibited its effectiveness in the shared task by earning the ninth rank out of all competing systems for the classification of abusive comments in the code-mixed text. Our proposed classifier achieves an impressive accuracy of 0.99 and an F1-score of 0.99 for a balanced dataset using TF-IDF with SVM. It can be used effectively to detect abusive comments in Tamil, English code-mixed text

## introduction in the age of rapidly advancing technology and increased social media usage, online platforms have become integral to our daily lives, facilitating global communication and connection @xcite @xcite @xcite @xcite . however, this interconnectedness has also led to the proliferation of abusive content and hate speech, posing severe challenges to maintaining a safe and respectful online environment @xcite @xcite @xcite . automated classification of abusive comments is paramount to curb the spread of harmful content and protect users from online harassment @xcite . moreover, abusive comments often emerge as a complex interplay of code-mixed text in multilingual communities, such as those in regions where tamil and english speakers coexist and interact. code-mixing is word or phrase level interchanging two or more languages within a single sentence or conversation @xcite . the prevalence of code-mixed text in social media interactions further intensifies the difficulty of abusive comment classification, as traditional natural language processing techniques may not be directly applicable @xcite @xcite . this research article addresses the challenges of abusive comment classification in tamil and english code-mixed text using machine learning techniques and the synthetic minority over-sampling technique (smote) to handle imbalanced data @xcite . we explore the effectiveness of employing smote to create a balanced dataset, thereby mitigating the skewed distribution of abusive and non-abusive comments. to achieve accurate and robust classification results, we explore two popular data representation techniques: bag-of-words (bow) and term frequency-inverse document frequency (tf-idf). bow represents text data by counting the occurrence of words in documents, while tf-idf considers the importance of words based on their frequency and rarity in the corpus @xcite . we apply these representations to our balanced dataset and utilize three different machine learning algorithms: support vector machines (svm), logistic regression (lr), and random forest (rf). one of the pivotal contributions of our research lies in showcasing the significance of smote in enhancing the performance of the classifiers. by addressing the class imbalance issue, smote enables the classifiers to better capture patterns and nuances related to abusive comments, improving classification accuracy and reducing the risk of misclassifying harmful content @xcite . our findings indicate that tf-idf representation consistently outperforms bow in svm, lr, and rf models. the ability of tf-idf to capture the semantic relevance and rareness of words provides a more substantial discriminatory power, leading to better classification results. leveraging tf-idf representation with smote-balanced data yields an accuracy demonstrating the potential for more effective content moderation in code-mixed text environments. however, despite the success achieved in our research, we recognize that addressing abusive comment classification in code-mixed text is an ongoing challenge. the dynamic nature of online content and the continuous evolution of abusive language demand continuous efforts in refining and updating our classification models. ## related works the growing presence of tamil and english, codemixed data on social media platforms has sparked increased research interest in addressing abusive comments. as the multilingual nature of codemixed content poses unique challenges, several studies have been conducted to develop effective methods for detecting and classifying abusive language in these contexts. the research by @xcite focuses on abusive comment detection in tamil using multilingual transformer models. they explored the effectiveness of multilingual transformer models in handling the complexities of tamil codemixed text for abusive comment detection. the results demonstrated the potential of transformerbased approaches in achieving accurate and robust classification, significantly promoting a safer online environment. the research contributes valuable insights to the field of natural language processing for dravidian languages, particularly in the context of abusive content moderation. the research conducted by prasanth et al. (2022) focuses on abusive comment detection in tamil language text using the tf-idf representation and the random kitchen sink algorithm. the authors addressed the challenge of identifying abusive content in the context of the tamil language, which is particularly relevant for content moderation and user safety on social media platforms. the tf-idf representation, known for capturing word importance and rarity, was combined with the random kitchen sink algorithm, a randomized feature mapping technique, to classify abusive comments. @xcite conducted research that focuses on the classification of abusive content in dravidian code-mixed text using a transformerbased approach. their methodology leverages advanced natural language processing techniques to address the challenges of code-mixing in tamil and other dravidian languages. the study aims to improve content moderation systems and create a safer online environment by accurately detecting and handling abusive comments in multilingual communities. the paper provides valuable insights into using transformers for abusive content classification in code-mixed text, contributing to the ongoing research efforts in natural language processing (nlp) and content moderation. @xcite conducted the study on detecting abusive comments in the tamil language using a transformer-based approach. transformers have proven to be highly effective in natural language processing tasks, and the authors explored their application for abusive comment classification in tamil. their work contributes to the growing body of research in content moderation and speech technologies for dravidian languages, addressing the crucial issue of abusive language online. the detailed methodology, experimental setup, and results presented in the paper offer valuable insights into the effectiveness of the proposed approach, providing a significant contribution to the field of nlp for tamil language processing. the research conducted by @xcite focuses on abusive comment detection in the tamil language. they employed a 1d conv-lstm model for classification. the study addressed the crucial issue of identifying abusive content in online tamil text, considering the challenges posed by multilingual code-mixing. the proposed model demonstrated promising results in detecting abusive comments, and its effectiveness was showcased during the shared task. it also provides insights into the approach, methodology, and results achieved, contributing to developing content mod-eration systems in multilingual environments. ## system description in this section, we provide comprehensive information about the dataset and elaborate on the experiments conducted in our study. figure 1 illustrates the system architecture designed for multiclass abusive comments classification using machine learning (ml) techniques, including smote. the diagram depicts the overall flow of the classification process. ## result in this research, we investigated the application of three machine learning algorithms, namely support vector machine (svm), logistic regression, and random forest, in conjunction with two data representation techniques: bag-of-words (bow) and term frequency-inverse document frequency ## conclusion in conclusion, this research delves into the critical issue of abusive comment classification in codemixed text, particularly in tamil and english codemixed data. the escalating growth of social media and online platforms has amplified the need for compelling content moderation to ensure a safe and respectful digital environment. we have achieved promising results in accurately identifying abusive comments by harnessing the power of machine learning techniques and smote for handling class imbalance. the utilization of svm, logistic regression, and random forest in combination with tf-idf representation has proven to be highly effective in capturing the nuances of abusive language, leading to improved classification accuracy. notably, the application of smote has significantly contributed to overcoming the challenges of class imbalance, enabling our models to make more reliable predictions. limitation is encountering out-of-vocabulary words or linguistic phenomena not accounted for during preprocessing. code-mixing can introduce linguistic variations that may not be adequately handled by the existing language processing techniques, leading to potential misclassifications. addressing these linguistic complexities in future research could enhance the model's performance and generalization capabilities. notably, in the shared task, our approach secured the 9th rank out of all participating systems. despite the competition's challenges and intense competition from other participants, our model's performance demonstrates its effectiveness in abusive comment classification in code-mixed text. future work investigating more advanced and context-aware feature extraction techniques will contribute to a more comprehensive analysis of code-mixed content. we continuously refine and update our models to develop robust and adaptive content moderation systems, creating a safer online environment for diverse, multilingual communities. we are optimistic that future work will address these limitations and elevate the performance of abusive comment classification in code-mixed text, contributing to a more inclusive and respectful online space for all users.