Habesha@ D ravidian L ang T ech: Abusive Comment Detection using Deep Learning Approach (2023)

Abstract: This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.

## introduction in recent years, online social networks (osns) have gained significant significance and have become a popular platform for obtaining news, information, and entertainment. however, despite the various advantages of utilizing osns, there is a mounting body of evidence indicating the presence of an escalating number of malicious individuals who exploit these networks to disseminate harmful content and cause damage to others. the negative consequences of these malicious activities are increasingly evident. the spread of poisonous content, such as hate speech, misinformation, and cyberbullying, can have severe psychological, emotional, and even physical effects on targeted individuals. moreover, the virality and reach of osns amplify the potential harm caused by malevolent actors, as harmful content can quickly spread across networks, reaching a vast audience and causing widespread damage. in order to mitigate this activity the organizer provide this shared task. natural language processing (nlp) focuses on the practical manipulation of textual components, converting them into a format suitable for machines. additionally, nlp plays a crucial role in artificial intelligence (ai) by providing vital insights to determine the positivity or negativity of information based on numerous comparisons. hence we combat to fix the above-mentioned problems by applying the nlp concept. ## related work the objective of this study @xcite was to explore the use of core text mining techniques in automatically detecting abusive content in various social media platforms such as blogs, forums, media-sharing sites, q and a platforms, and chat services. the research utilized datasets from popular platforms like twitter, youtube, myspace, kongregate, formspring, and slashdot. by employing supervised machine learning, the study compared different text representations and dimension reduction methods, including feature selection and feature enhancement. the results demonstrated the significant influence of these techniques on the accuracy of abusive content detection. ultimately, the researcher concluded that employing a balanced dataset positively impacts the accuracy of detecting abusive content on social media platforms. they conducted to use minority class and majority class and obtain the best result on the minority class. in addition to that using feature reduction will improve efficiency whilst maintaining detection accuracies. awal et al. (2018) designed to detect abusive comments in social media by using naïve bayes. the researcher collected the corpus from youtube. to calculate the occurrence of particular words in a particular comment used a bag of words (bow) vector. in order to evaluate the performance of the model they applied 10-fold cross-validation. the researcher @xcite undertook a comprehensive study focusing on the detection of abusive language in both urdu and roman urdu comments. this investigation encompassed the utilization of a diverse set of machine learning and deep learning models. specifically, the author employed five ml models, namely naive bayes, support vector machine, instance-based learning , logistic regression, and jrip. additionally, four dl models, including cnn, lstm, blstm, and convolutional lstm, were also harnessed in the analysis. the research methodology consisted of applying these models to two distinct datasets: a sizable collection comprising tens of thousands of roman urdu comments, and a comparatively smaller dataset containing over two thousand comments in urdu. the primary objective was to assess the performance of these models in both linguistic variations. the outcomes of the experiments conducted revealed noteworthy insights. notably, the cnn exhibited superior performance compared to the other models. impressively, it achieved accuracy rates of 96.2% for urdu comments and 91.4% for roman urdu comments. this marked the cnn as the most adept model in accurately identifying abusive language within these linguistic contexts. this study @xcite delves into the crucial task of identifying various forms of abusive content within the realm of online platforms. the research extensively explores the utilization of diverse machine learning and deep learning methodologies to address this challenge. the algorithms under scrutiny encompass a range of models, including the linear support vector classifier (lin-earsvc), logistic regression (logit), multinomial naïve bayes (mnb), random forest (rf), artificial neural network (ann), and a rnn featuring alstm cell. this author introduces a pioneering dimension by devising novel stemming rules tailored specifically for the bengali language. these rules substantially contribute to enhancing the efficacy and overall performance of the algorithms employed in the study. notably, the deep learningpowered rnn model emerges as the frontrunner among the examined algorithms, boasting an impressive peak accuracy rate of 82.20%. this scholarly investigation involves a comprehensive evaluation of the efficacy of deep learning (dl) models in contrast to machine learning (ml) models for the purpose of detecting instances of abusive language. the researcher, a notable contributor in this domain, undertook an empirical study wherein a comparative analysis was conducted between logit and blstm models. the primary objective was to discern their effectiveness in identifying abusive language within the context of the danish language. the study's findings showcase a distinct trend: the blstm model, a sophisticated variant of recurrent neural networks, emerged as the standout performer. it outshone the competing models in terms of accurately categorizing comments sourced from prominent social media platforms such as reddit, facebook, and twitter.(sigurbergsson and derczynski, 2019) comprehensive experimentation and analysis revealed that the blstm model exhibited remarkable capabilities in dealing with the intricacies and nuances of abusive language present in user-generated content. the studies discussed earlier have provided us with a valuable insight, demonstrating that the utilization of deep learning methodologies offers a straightforward means of detecting the intended content. what's even more advantageous about employing deep learning is that it eliminates the necessity for employing supplementary feature extraction techniques. this implies that the inherent capability of deep learning models allows them to discern patterns and features directly from the data, obviating the need for manual feature engineering. the integration of deep learning into content detection thus emerges as a pivotal advancement in the field, heralding a new era of intelligent and efficient detection mechanisms. this transformative approach holds promise for a wide array of applications where accuracy and automation are paramount. ## task description the objective of the task is to determine whether a comment includes any form of abusive content. the data sets consist of youtube comments written in the tamil and telugu-english languages. the comments or posts in the corpus can consist of multiple sentences, but the average sentence length of the corpus is one. the annotations in the corpus are made at the comment or post level, rather than at the sentence level. this means that the task involves analyzing comments or posts as a whole to determine if they contain abusive content, rather than focusing on individual sentences within the comment. the annotations or labels indicating whether a comment is abusive or not are assigned based on the overall content of the comment or post @xcite . ## result and discussion according to the findings presented in in the second experiment, our primary objective was to detect tamil-english and telugu-english languages using an lstm model. the results demonstrated that the lstm model performed better in the telugu-english detection task, achieving a precision of 0.65, a recall of 0.65, and an f1 score of 0.65. on the other hand, for the tamil-english detection, the lstm model achieved a precision of 0.27, a recall of 0.25, and an f1 score of 0.26. the performance metrics offer valuable insights into how effectively the models can identify in-stances of abusive language in tamil-english and telugu-english texts. the higher precision, recall, and f1-score in the telugu-english detection task indicate that the lstm model exhibited superior performance in accurately identifying abusive language in that language pair. however, it is important to note that the model's performance was relatively lower in the telugu-english detection task, suggesting the need for further refinements and improvements in the model's ability to identify abusive language in that specific language pair. ## conclusion the research paper utilizes deep learning models, specifically lstm and rnns, to analyze language patterns. the lstm model is employed to enhance contextual understanding by capturing long-term dependencies and intricate patterns in the input sequences. when detecting abusive language in telugu and tamil-english, an lstm model is utilized, resulting in improved accuracy and a deeper understanding of the context. in the context of tamil abusive language detection, a word-level rnn is created and trained to identify abusive words. this type of rnn processes text sequentially, capturing contextual dependencies and patterns within the sequence of words. to attain enhanced and superior performance levels, our focus will center on the utilization of a transformer-based approach @xcite . our strategy involves intricately configuring the parameters of this approach, meticulously fine-tuning them to yield outcomes that hold great promise. this deliberate effort aims to extract the utmost potential from the transformer model, thereby optimizing its performance for optimal results. through this approach, we intend to unlock new dimensions of efficiency and effectiveness, pushing the boundaries of achievement in our pursuit of excellence.