bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 547–550 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 547 bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media Ritesh Kumar Department of CSE IIT(ISM) Dhanbad India, 826004 ritesh4rmrvs@gmail.com Guggilla Bhanodai Department of CSE IIT(ISM) Dhanbad India, 826004 bhanodaig@gmail.com Rajendra Pamula Department of CSE IIT(ISM) Dhanbad India, 826004 rajendra@iitism.ac.in M. R. Chennuru Department of CSE IIT(ISM) Dhanbad India, 826004 cmr.mahesh@gmail.com Abstract This paper describes the work that our team bhanodaig did at Indian Institute of Technol- ogy (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have par- ticipated in sub-task B: automatic categoriza- tion of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classifica- tion. The official ranking metric is macro- averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new en- trant to the field, our scores are encouraging enough to work for better results in future. 1 Introduction Social media has become most popular among users in these days. Based on survey (Johnson et al., 2011), it has been observed that 70% of teenagers use social media sites on daily basis. Users share their views with help of social media like twitter, facebook, instagram, youtube. Ritesh et al. (Kumar et al., 2018a) tried to identify hate speech. On the one hand Users get benefited from social media by learning or interacting with other users on the other hand they face offensive online contents. With exponential growth of social me- dia it has become quite significant to identify and categorize offensive language in social media. A key challenge among researchers is to auto- matically categorization of offense type languages in social media. few research have been performed but it is still a hot topic among researchers. keep- ing it in mind, we develop a system that could cat- egorize offensive language in social media. The relevant shared task description, data and results are described in the paper (Zampieri et al., 2019b). In this paper, we use Linear Support Vector Ma- chine (LSVM) for classifying and identifying of- fensive language in social media. We use snowball stemmer to find out root words. Also, we have used unigram and bigram language models with- out stopwords. The rest of the paper is organized as follow. Section 2 describes related work. The proposed methodology and used data is described in section 3. Section 4 describes results obtained after ex- periment. Finally, we conclude and future work in section 5. 2 Related Work The interest in identifying and categorizing ag- gression, cyber-bullying and hate speech, partic- ularly on social media, has been growing in recent years. This topic has attracted attention from re- searchers interested in linguistic and sociological features of aggression, and from engineers inter- ested in developing tools to deal with aggression on social media platforms. …

Content truncated to 500 words