J udith J eyafreeda A ndrew@ D ravidian L ang T ech- EACL 2021:Offensive language detection for D ravidian Code-mixed Y ou T ube comments (2021)

Abstract: Title: JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments Author: Judith Jeyafreeda Andrew Messaging online has become one of the major ways of communication. At this level, there are cases of online/digital bullying. These include rants, taunts, and offensive phrases. Thus the identification of offensive language on the internet is a very essential task. In this paper, the task of offensive language detection on YouTube comments from the Dravidian lan- guages of Tamil, Malayalam and Kannada are seen upon as a mutliclass classification prob- lem. After being subjected to language spe- cific pre-processing, several Machine Learn- ing algorithms have been trained for the task at hand. The paper presents the accuracy results on the development datasets for all Machine Learning models that have been used and fi- nally presents the weighted average scores for the test set when using the best performing Ma- chine Learning model.

## introduction with the growing freedom on the internet, facing digital bullying has become a daily phenomenon. offensive languages can be found everywhere including comments on social media. these text are more often targeted to an individual or to a group. the task presented in this paper is to identify offensive language in youtube comments and classify them in different categories. the comments are in code-mixed dravidian languages of tamil, malayalam and kannada. tamil is a dravidian language natively spoken by south asia's tamil people @xcite . for over 2600 years, tamil literature has been recorded. sangam literature, the oldest period of tamil literature, is dated from ca. 600 bc-300 ad. among the dravidian languages, tamil has the oldest existing literature. over 55 percent of the epigraphic inscriptions discovered by the archaeological survey of india (about 55,000) are in the tamil language. malayalam is tamil's nearest major relative; the two started diverging around the 16th century ad. code-mixing is mixing of two or more language in the same utterance. many user generated in india are code-mixed @xcite . most of the comments contain multiple types of offensive contents. for this purpose a multiclass classification method has been adapted to the task. multiclass text classification is a process of classifying an instance into one of the multiple classes possible. in a multi class classification problem, an instance can belong only to one class. several machine learning algorithms have been well established for multiclass classification @xcite . however, not all of them suit the task at hand. it also has to be noted that machine learning algorithms has to be tuned to fit the dravidian languages under consideration (tamil, malayalam and kannada). thus several pre-processing techniques have been proposed for dravidian languages and machine learning algorithms have been fine tuned to suit the task @xcite @xcite . ## related work offensive language detection is one of the interesting topics where a lot of research has already been done. however, they have all been language specific. @xcite has used a supervised learning approach with the context, sentiment and contextual features of the document for identifying harassment on the web. @xcite also use supervised learning techniques with three important features: content-based features, cyberbullying features and user-based features. the contentbased are based on the content of the text, the cyberbullying features aim to identify frequently bullied groups such as minority races, religion or physi-cal features, the user-based features exploits the user identity. @xcite uses pattern recognition and machine learning methods for offensive language detection. this method extracts features in different conceptual levels and applies a multilevel classification on them. @xcite presents smokey, which is a system built for automatic recognition of hostile messages. (ming xu et al.) uses a combination of text categorization, role labelling, sentiment analysis and topic modelling for identifying bullying on social media data. @xcite uses common sense reasoning to identify and mitigate cyberbullying. . for this purpose, it uses a common sense knowledge base is used, which permits recognition over a broad spectrum of topics in everyday life. @xcite concentrates on a more narrow range of subject matter associated with bullying like appearance,intelligence, racial and ethnic slurs, social acceptance, and rejection and constructs bullyspace, which is a commonsense knowledge base that encodes particular knowledge about bullying situations. in this paper, the task of offensive language is approached as a multiclass classification problem. @xcite compares various machine learning algorithms -naïve bayes, random forest, decision tree, logistic regression and support vector machines for the classification of text reviews. the findings indicate that the logistic regression for multi-class classification for product reviews is the best method in terms of accuracy. it should also be noted that the overall classification accuracy in combination with uni/bi/tri-gram models increases the average of classification accuracy. with respect to research works done on dravidian languages, particularly dravidian code mixed text, a shared task has been proposed for the task of sentiment analysis of youtube comments in dravidian code-mixed text @xcite . @xcite presents an improvement of word sense translation for under-resourced languages. it focuses on cleaning the noisy corpus in the form of code-mixed content at word-level based on orthographic information which results in improvement of dravidian languages. it also proposes to alleviate the problem of different scripts by transcribing the native script into a common representation such as the latin script or the international phonetic alphabet (ipa). @xcite proposed a multiclass classification method, where several machine learning algorithms have been adapted to the task of sentiment analysis and based on the accuracy of the algorithms on the development set the best suited technique is chosen for the language and the task. the languages involved are the dravidian languages of tamil and malayalam. the tamil language performed well with the naive bayes algorithm while the malayalam language performed well with the logistic regression technique. ## data the data for the shared task of offensive language detection in dravidian code-mixed languages (chakravarthi et al., 2021) is a collection of youtube comments. the languages used are tamil, malayalam and kannada. the tamil code-mixed youtube comments are obtained from @xcite . the malayalam code-mixed youtube comments are obtained from @xcite . the kannada code-mixed youtube comments are obtained from @xcite . the classes are "notoffensive", "offensive-untargeted", "offensivetargeted-individual", "offensive-targeted-group", "offensive-targeted-other", or "not-in-indentedlanguage". the data set for the tamil code-mixed youtube comments has 35,139 instances in the train set, 4,388 instances in the dev set and 4,392 instances in the test set. the data set for the malayalam code-mixed youtube comments has 16,010 instances in the train set, 1,999 instances in the dev set and 2,001 instances in the test set. the data set for the kannada code-mixed youtube comments has 6216 instances in the train set, 776 instances in the dev set and 778 instances in the test set. ## pre-processing the dravidian languages used needs some preprocessing in order to be able to adapt to machine learning algorithms. the pre-processing used in this paper are as follows: for the purpose of training a supervised classifier, each youtube comment in the dataset is represented by a numerical feature vector. one common approach for extracting features from text is to use the bag of words model. in this model, the frequency of the words is taken into consideration, but the order in which they occur is ignored. the term frequency, inverse document frequency (tf-idf) measure is calculated for each term in the dataset (individually for tamil, malayalam and kannada). ## implementation the sklearn 1 package in python is used for the feature extraction and model training. the models of logistic regression, linear support vector classification, multinomialnb, knn, decision trees and random forest provided by the sklearn toolkit are used for the training of the machine learning 1 https://sklearn.org/ models discussed in section 5. the implementation details for these models are shown in as seen from