O ff T amil@ D ravidean L ang T ech- EASL 2021: Offensive Language Identification in T amil Text (2021)

Abstract: In the last few decades, Code-Mixed Offensive texts are used penetratingly in social media posts. Social media platforms and online communities showed much interest on offensive text identification in recent years. Consequently, research community is also interested in identifying such content and also contributed to the development of corpora. Many publicly available corpora are there for research on identifying offensive text written in English language but rare for low resourced languages like Tamil. The first code-mixed offensive text for Dravidian languages are developed by shared task organizers which is used for this study. This study focused on offensive language identification on code-mixed low-resourced Dravidian language Tamil using four classifiers (Support Vector Machine, random forest, k- Nearest Neighbour and Naive Bayes) using chiˆ2 feature selection technique along with BoW and TF-IDF feature representation techniques using different combinations of n-grams. This proposed model achieved an accuracy of 76.96% while using linear SVM with TF-IDF feature representation technique.

## introduction offensive language is the key concern of technical companies nowadays due to exponential growth in number of internet users around the world and since these people are from different culture, race, religion, origin, gender and nationality @xcite . internet gives more freedom to people to express their opinions freely in different forms such as blogs, forums and social media platforms (e.g., facebook, twitter, and youtube) @xcite . it is noted that the usage of social media among the people has increased rapidly since last decade @xcite . people can express their opinion in a posi-tive way as well as negative way @xcite . so the offensive comments are now avoidable in those platforms, the problem has to be solved. it has a negative impact on society and individuals @xcite @xcite . there are huge amount of researches are found in identifying the offensive words in english language. there are so many publicly available corpora in english language. people from multilingual society will add comments and reviews with the mixing of vocabulary and syntax of multiple languages in the same sentence @xcite . so it is a big challenge to identify the offensive words from the dravidian languages @xcite . with a history stretching back to 600 bce, the tamil language is one of the world's longest-surviving classical languages. poetry, especially sangam literature, which is made up of poems written between 600 bce and 300 ce, dominates tamil literature. all the dravidian languages evolved from tamil language. the first attempt to create this offensive language has done by @xcite . we used code-mixed texts from the youtube reviews as a corpus. in this paper, we proposed a method using linear svm with chi 2 feature selection based approach to find offensive language in tamil language @xcite . the rest of the paper is organised as follows. section 2 describes related works. section 3 presents proposed method. section 4 presents experimental setup and the results. finally, discussion and conclusion are in section 5. ## related work detecting offensive language is not an easy task. many researchers has proposed many methods and algorithms to detect offensive language content on the web. @xcite trained a support vector machine (svm) classifier using world-level features (with and without preprocessing), svm (with and without the normalisation), n-gram level features. they used arabic language corpus which was pre-processed with tokenization, filtering and normalisation. their classifier achieved an accuracy of 90.05 upon using 10-fold cross-validation. it has been observed that the n-gram features improves the classifier's performance. on the contrary, the combination of stemming and n-gram features harms precision. ibrohim and budi (2018) used machine learning approach with simple word n-gram and character n-gram features and trained naïve bayes, support vector machine, and random forest decision tree classifiers. discussed abusive language detection in the indonesian language corpus. 10-fold cross validation technique is used to evaluate the classification result. if the corpus labeled with three classes(non-abusive language, abusive language but not offensive and offensive language) naïve bayes classifier with the combination of word unigram and bi-grams features gives the best result 70.06% of f1-score. if the corpus labeled with two classes(abusive language or non-abusive language) then all the classifiers gives higher results. the have concluded that the classifying into three classes is more difficult than just classifying abusive language or non-abusive language. @xcite analyzed the impact of various extra-linguistic features in conjunction with character n-grams for hate speech detection. corpus is normalized for pre-processing. uni grams, bi grams, tri grams and four grams were collected for each tweets. logistic regression classifier and 10-fold cross validation were used to test the influence of various features on prediction. found that character n-grams of length up to 4 along with gender as an additional feature provides the best results. nayel and @xcite in this research they have used corpus in three languages (english, germany and hindi). tf-idf vectors has been computed for all the posts in the training set. for pre-processing all un-informative tokens such as urls, digits and special characters have been removed from all the posts. trained using linear classifier, svm and mlp classifier, tested with 5-fold cross-validation approach. classified as three tasks as a, b and c respectively. unlabeled instance into one of the two predefined categories classified for task a, unlabeled instance into one of the three predefined categories classified for task b and same instance into one of the two predefined categories classified for task c. concluded that for english language svm outperforms for all three tasks. for german language mlp out performs for task a and b. for hindi language mlp outperforms for task a and svm gives better results for task b. chakravarthi et al. (2020b) created a corpus containing 15744 youtube comments and posts as a code-mixed dataset. following classifiers are used to classify the corpus support vector machine(svm), logistic regression (lr), k-nearest neighbour (k-nn), decision tree, random forest, multinomial naive bayes,bert multilingual, 1dconv-lstm, dme, cdme. they have concluded that random forest classifier, logistic regression and decision tree gives the best results. chakravarthi et al. (2020a)analysed in codemixed dravidian text from social media that aims at classifying youtube comments. the hundred and nineteen teams partici-pated in the task, and a total of 32 teams for tamil and 28 teams malayalam submitted the results.they trained on the unbalanced dataset. the methods proposed by participants ranged from traditional machine learning models with features based approaches to using state-ofthe-art embedding methods in deep learning models.the best performing run achieved weighted f1score of 0.65 and 0.74 for tamil and malayalam respectively. ## methodology offensive language identification aims to identify the offensive text written in tamil language. in this paper we experimented a method using four classifiers and χ 2 feature selection technique to identify the offensive text written in tamil documents. the overall framework of offensive language identification is shown in figure 1 . the methodology applied in this research is divided into four parts. subsection 3.1 describes the corpus used, subsection 3.2 describes the preprocessing applied, subsection 3.3 describes the feature selection and representation techniques and the subsection 3.4 describes classifiers used. ## experimental setup and results test results of feature-set-1 with bow and tf-idf feature representation techniques of four classifiers are listed in it is observed that from the table that linear svm performs better than other three classifiers for this corpus while using feature-set-1. therefore we continued our experiments using linear svm. results of bow feature representation technique with varying values of features for all three feature sets are shown in test results of linear svm with tf-idf feature representation technique are shown in in this paper we proposed χ 2 feature selection technique based offensive language identification method. we compared results of four classifiers and observed that linear svm outperformed all other classifiers tested here. moreover we have checked the influence of different feature sets and found that feature-set-1 performs better than other two feature sets for both feature representation techniques. another finding of this research is that we can be able to get better results with least number of features while using χ 2 feature selection technique. the highest accuracy of 76.96% is obtained while using tf-idf feature representation with linear svm. more over we obtained highest accuracy while using 3500 features as vocabulary for both bow and tf-idf feature representation techniques.