PANDAS @Abusive Comment Detection in T amil Code-Mixed Data Using Custom Embeddings with L a BSE (2022)

Abstract: Abusive language has lately been prevalent in comments on various social media platforms. The increasing hostility observed on the internet calls for the creation of a system that can identify and flag such acerbic content, to prevent conflict and mental distress. This task becomes more challenging when low-resource languages like Tamil, as well as the often-observed Tamil-English code-mixed text, are involved. The approach used in this paper for the classification model includes different methods of feature extraction and the use of traditional classifiers. We propose a novel method of combining language-agnostic sentence embeddings with the TF-IDF vector representation that uses a curated corpus of words as vocabulary, to create a custom embedding, which is then passed to an SVM classifier. Our experimentation yielded an accuracy of 52% and an F1-score of 0.54.

## introduction in recent times, with rapid digitisation, people are increasingly using social media and various other forums available online for interpersonal communication @xcite . however, these platforms also come with their own share of drawbacks, such as the propagation of fake news @xcite and cyberbullying @xcite , to list a few. comments that are found to be offensive and often degrading, that may be targeted at an individual or a community as a whole, are categorised as abusive comments. these comments often have negative effects on the mental well-being of people (o @xcite , with an apparent relation between the time spent on social media and increasing levels of depression @xcite . there is a pressing need for moderation on these websites, which motivates the creation of a system that will be able to classify abusive comments into one of many categories. it could also be useful in identifying and filtering out vitriolic content. a major challenge faced with this task is that most of the data available contains a mixture of languages @xcite , with people often transliterating from their native language into english, thus posing a hurdle, as most resources available for the task of abusive language detection are pretrained on english text. tamil is a dravidian classical language used by the tamil people of south asia. tamil is an official language of tamil nadu, sri lanka, singapore, and the union territory of puducherry in india. tamil is one of the world's longest-surviving classical languages. malayalam is tamil's closest significant cousin; the two began splitting during the 9th century ad @xcite @xcite @xcite @xcite @xcite @xcite . the task a of abusive comment detection in tamil-acl 2022 @xcite involves classification of purely tamil text, whereas task b deals with the classification of code-mixed tamil english text into 8 categories as listed in the remainder of this paper is organised as follows. section 2 is dedicated to related works obtained from the literature survey. section 3 proceeds to describe the dataset used. section 4 covers the details of the preprocessing steps, outlines the feature extraction process and describes the model employed for this task. section 5 summarises the results and section 6 concludes the paper. 2 related works identification and classification of offensive tasks in a fast and effective manner is very important in the moderation of online platforms (priyadharshini et al., 2021; kumaresan et al., 2021; chakravarthi, 2020; chakravarthi and muralidaran, 2021; sampath et al., 2022; ravikiran et al., 2022; chakravarthi et al., 2022; bharathi et al., 2022). we explored various models to achieve the same. ravishankar et al. (ravishankar and raghunathan, 2017) proposed three different approaches to classify tamil tweets based on syntactic patterns. these include tweet weight model, tf-idf and domain-specific tags (dst), and used tamil dictionary (agarathi).the authors collected tweets from 100 movies which amounted upto 7000 tweets. they proposed three other feature extraction models which include tf-idf, adjective rules, negation rules, and adjective rules which could be passed into classifiers. alison p. ribeiro et al.(ribeiro and silva, 2019) presented their model to identify hate speech against women and immigrants. it consisted of pre-trained word embeddings using fasttext and glove which they passed through a cnn network. younes samih et al. @xcite modelled an architecture with support vector machine (svm) and deep neural networks (dnns) for task to identify hate speech and offensive content. they experimented four different approaches and combined them into an ensemble. they used fast-text for the first one, ffnn architecture with four hidden layers for the second one and for the third one they created pretrained word embeddings using mazajak method which was then passed into a cnn layer and a bilstm layer. their next approach was using bert. they combined these to create an ensemble which performed well for the given dataset. anna glazkova et al. @xcite for the hasoc 2021 task which focused on detecting offensive, profane and hate content in tweets in six languages . they proposed various models which include pretrained bert, roberta and labse. though the performance of the models were similar for the english datasets, labse outperformed the others for hindi and marathi datasets. shervin et al @xcite ) used a corpus of 14.5k english tweets and modelled an approach to classify them as hate speech and nonhate speech. their model uses character n-grams, word n-grams and word skip-grams for feature extraction which was passed onto a linear svm classifier. burnap et al. in their paper (burnap and williams, 2014) wrote about their model -they used unigram , bigram feature extraction techniques and pos (parts of speech tagging), they also used the stanford lexical parser, along with a context-free lexical parsing model, to extract typed dependencies within the tweet text. this was further passed to classifiers like bayesian logistic regression, random forest decision trees and support vector machines. aswathi saravanaraj et al. proposed an approach for the automatic identification of cyberbullying words and rumours. they modelled a naive bayes and a random forest approach which obtained a greater accuracy then pre-existing models. from the literature survey performed, it is inferred that an approach involving feature extraction using tf-idf delivers good results and that transformer models like labse work the best for indian language datasets, with a particularly high accuracy for the tamil language. the svm classifier works well for hate/abusive language recognition. although various innovative models have been experimented on in the studies discussed above, a model involving tf-idf feature extraction, labse and svm, will be a novel approach to this task. ## dataset the dataset used for the study is made up of comments made by subscribers in tamil language, in relation to a video available on the streaming platform youtube. the text contents were retrieved and stored following manual annotation. the text statements were organized into 8 different classes: transphobia, counter-speech, misandry, homophobia, hope-speech, xenophobia, misogyny and noneof-the-above, depending on the sentiment reflected through them. the dataset has a highly disproportionate share of expressions being brought under 'none-of-the-above' -62% from the train dataset and 61% from the development dataset. the data distribution of the train and development datasets is depicted in ## methodology the proposed methodology for this task involves extracting lexical and sentence features from the data and applying classifier models, such as svm, mlp and k neighbours classifier, to them. this is illustrated in figure 1 . ## conclusion this paper discusses our approach for the dra-vidiantechlang acl 2022 shared task, which aims to identify and classify abusive content in tamil-english code-mixed text collected from social media. this research contributes to this task by analysing a set of classification models for identifying various types of abusive comments. we have used a combination of embeddings using tf-idf and labse with the svm classifier. our results showed that using this pre-trained multilingual model along with the svm classifier yielded better results for the code-mixed data. this model gave us a macro f-1 score of 0.49 and an accuracy of 0.74 on the development dataset, and an accuracy of 0.520 and a weighted f1-score of 0.54 on the test dataset. in the future, we would like to improve our results by using better preprocessing techniques, which may be achieved by acknowledging and utilising the significance of relevant special characters and emoticons in the given text, instead of removing them altogether.