KEC _ AI _ NLP @ D ravidian L ang T ech: Abusive Comment Detection in T amil Language (2023)

Abstract: Our work aims to identify the negative comments that is associated with Counter-speech,Xenophobia, Homophobia,Transphobia, Misandry, Misogyny, None-of-the-above categories, In order to identify these categories from the given dataset, we propose three different models such as traditional machine learning techniques, deep learning model and transfer Learning model called BERT is also used to analyze the texts. In the Tamil dataset, we are training the models with Train dataset and test the models with Validation data. Our Team Participated in the shared task organised by DravidianLangTech and secured 4th rank in the task of abusive comment detection in Tamil with a macro- f1 score of 0.35. Also, our run was submitted for abusive comment detection in code-mixed languages (Tamil-English) and secured 6th rank with a macro-f1 score of 0.42.

## introduction social media platforms that provide communications, education, and information exchange in the digital age have become an essential part of our lives. but this increased online communication has also raised concerns about the spread of slander, hate speech, and online bullying @xcite . slander is a statement made against a person or group of people. in recent years there has been a growing awareness of the harm that negative comments (narang and brew, 2020) can do to individuals and society as a whole. insults not only cause psychological harm to the victim, but also 1 https://codalab.lisn.upsaclay.fr/competitions/11096 cause the spread of humor, division, and dissatisfaction online. given the detrimental impact hate speech has on the general public, major platforms such as youtube, facebook, instagram, and twitter have implemented policies @xcite and protocols to address hateful content and combat negative behaviors. it is crucial to prioritize the identification and management of such comments to minimize their adverse effects on individuals. over the past few years, a growing body of research has been addressing the issue of tackling abusive comments @xcite in the fields such as natural language processing (nlp), network science, and artificial intelligence (ai). early studies relied on machine learning (ml) classes such as support vector machine (svm) with word and attribute n-gram features and logistic regression (lr) @xcite . analyzing the illegal language in tamil requires a good understanding of the features of the language, its cultural context, and certain language patterns associated with abusive content. this has required the development of tamil-adapted artificial intelligence algorithms and machine learning models that can identify and neutralize illegal words with higher accuracy @xcite . machine learning models that possess specific options share similarities and present a simpler alternative to transformer models @xcite . recent studies on advancements in offensive language detection indicate a growing trend in utilizing deep learning-based transformer models @xcite . transfer learning is a concept where models are initially trained on extensive sets of unlabeled text using self-supervised learning, and subsequently employed on labeled text corpora. for our project, we employ the bidirectional encoder representation from transformers (bert) model @xcite . our task is to analyze and detect abusive com-ments in tamil @xcite . tamil has an agglutinative grammar, that is, the last word is used to denote class, number case, verb tenses, and other grammatical forms. abusive comment detection is a text classification problem @xcite ). text classification is a technique for extracting features from the text, giving it a preset category. it is always done by the linear classifier of sentence embeddings of text. in this paper, we trained various machine learning models, deep learning, and transfer learning models for detecting abusive comments in the tamil language. we compared the results of all the methods to determine the best model. the paper by @xcite highlight the challenges associated with arabic abusive language detection, such as the presence of dialects and informal language usage. the review in @xcite provides valuable insights into the state-of-the-art methods, data sets, and evaluation metrics used in this domain. the paper by mishra et al. (naacl 2019) presents a novel approach for abusive language detection using graph convolutional networks (gcns). the authors propose a graph-based model that captures both syntactic and semantic dependencies between words in a sentence to effectively identify abusive content. (mishra et al., 2019)leverage graph convolutional layers to learn contextual representations from the sentence's dependency graph, enabling the model to capture the structural information crucial for identifying abusive language. the paper by park et al. (emnlp 2018) addresses the issue of gender bias in abusive language detection systems. the authors propose a method to reduce gender bias by incorporating gender information into the training process. (park et al., 2018) introduce a gender-balanced data set and develop a fine-tuning strategy that takes into account the gender of both the author and target of abusive language. the experimental results show that their approach effectively reduces gender bias in the detection system while maintaining good overall performance. the paper by ibrohim and budi (alw 2019) focuses on the task of multi-label hate speech and abusive language detection in indonesian twitter. the authors present a comprehensive analysis of various approaches for addressing this challenge, including feature-based, deep learning, and ensemble methods. ## dataset description the goal of this shared task on abusive comment detection is to detect and reduce abusive comments on social media. the dataset used here is shared by the shared task @xcite . the primary goal of this project is to develop methods for detecting and classifying instances of hate speech in tamil. the abusive comment detection data set is made up of tamil comments retrieved from the youtube comments area @xcite . the data set consists of a comment and its related label from one of the nine labels: misandry, counter-speech, misogyny, xenophobia, hope-speech, homophobic, transphobic, not-tamil, and none-of-the-above.smote, which stands for synthetic minority over-sampling data augmentation technique, is a widely used technique in the field of machine learning specifically in the context of handling imbalanced datasets.imbalanced datasets occur when the classes have significantly different numbers of instances, leading to a bias in the model's performance towards the majority class. smote is designed to solve this issue by generating synthetic samples for the minority class, thereby balancing the class distribution.synthetic samples are artificially created data points that are generated based on existing data points in a dataset.in our dataset,'misandry' belongs to the minority class,thereby we use smote technique to balance the class distribution. ## performance evaluation from the comprehensive results presented in with its ability to leverage an ensemble of decision trees and feature importance estimation, the random forest model demonstrated its prowess in capturing complex patterns within the data set and making accurate predictions. the performance advantage of the random forest model can be attributed to its ability to handle high-dimensional data, effectively deal with noisy and missing values, and mitigate overfitting concerns. by leveraging a combination of feature subsampling and bootstrap aggregating, the model achieved robust generalization and reduced the risk of overfitting. the deep learning model, which often requires significant computational resources and extensive parameter tuning, fell short in this evaluation. it might have struggled to extract meaningful representations from the given data set or faced challenges in optimizing its numerous parameters, thereby resulting in comparatively lower accuracy and f1 score. similarly, the transfer learning model, which typically leverages pre-trained neural networks and fine-tunes them for specific tasks, failed to outperform the random forest model. although transfer learning has proven to be effective in various domains, it appears that the unique characteristics of the data set did not lend themselves well to this particular transfer learning approach. ## conclusion the study focuses on the detection of abusive comments in the tamil language. we compare the performance of different models in this task. deep learning and transformer models did not achieve superior results when trained and evaluated on tamil data. instead, machine learning models outperformed the deep learning and transformerbased models. it is important to note that contextualized embeddings such as elmo or flair, which have shown potential in enhancing the performance of language models has not been utilized. the absence of these embeddings might have limited the effectiveness of the models used in our study. we acknowledge this limitation and suggest that future work should explore the implementation of contextualized embeddings using deep learning techniques. we believe that incorporating these advanced embeddings could potentially improve the detection of abusive comments in tamil. additionally, other models like indic bert and muril bert were not utilized at this stage. however, we highlight the possibility of implementing these models in the future as our project advances. this indicates the potential for further exploration and improvement in the detection of abusive comments in tamil using more advanced language models.