TUVD team at SemEval-2019 Task 6: Offense Target Identification

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 770–774 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 770 TUVD team at SemEval-2019 Task 6: Offense Target Identification Elena Shushkevich Social Media Research Group Technological University Dublin, Ireland e.shushkevich @yandex.ru John Cardiff Social Media Research Group Technological University Dublin, Ireland john.cardiff @it-tallaght.ie Paolo Rosso PRHLT Research Center Universitat Politecnica de Valencia, Spain prosso@dsic.upv.es Abstract This article presents our approach for detect- ing a target of offensive messages in Twit- ter, including Individual, Group and Others classes. The model we have created is an en- semble of simpler models, including Logistic Regression, Naive Bayes, Support Vector Ma- chine and the interpolation between Logistic Regression and Naive Bayes with 0.25 coeffi- cient of interpolation. The model allows us to achieve 0.547 macro F1-score. 1 Introduction Nowadays aggressive language on social media occurs more and more often. Categories of hate speech can be very diverse and can deal with a wide range of issues such as misogyny, sexual orientation, religion and immigration. Such types of speech can be found in posts in social networks, in Internet discussions, in comments on various articles and in responses to posts of famous persons. This problem is receiving increasing amounts of attention and researchers are making attempts to build systems capable of recognizing such kinds of aggressive speech, offenses and insults in social networks. This article presented our approach to hate speech detection, which we used for the challenge SemEval-2019 Task 6: OffensEval - Identifying and Categorizing Offensive Language in Social Media (Zampieri et al., 2019a),(Zampieri et al., 2019b). The task consisted of three sub-tasks and proposed to investigate the data extracted from Twitter for creating a classification system. Sub-task A had the aim to identify offensive language and there were 860 unmarked English tweets for testing. The post had to be non offen- sive if it did not contain any offense or profanity. The main goal of the Sub-task B was to cat- egorize offensive posts from Sub-task A (there were 240 English tweets for testing) to different offensive types: - Targeted Insults and Threats in cases when a post insults or treats to an individual, a group or an organization; - Untargeted in cases where a post has a non-targeting profanity and swearing. Sub-task C focused on offense target identifica- tion. There were 213 English tweets which were marked as offensive in Sub-task A and Targeted Insult and Threats in Sub-task B for testing. The classification was for three different groups: - Individual, when the target of the offensive post was a person; - Group, when the target of the offensive message was a group of people considered as a unit; - Other, when the target of the offensive tweet did not belong to any of the previous cate- gories (e.g., a situation, an event, or another issue). There are two datasets in English and in Span- ish languages for analysis, and our team worked with English only. The training …

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