Pin_cod_ at SemEval-2020 Task 12: Injecting Lexicons into Bidirectional Long Short-Term Memory Networks to Detect Turkish Offensive Tweets

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 2117–2122 Barcelona, Spain (Online), December 12, 2020. 2117 pin cod at SemEval-2020 Task 12: Injecting Lexicons into Bidirectional Long Short-Term Memory Networks to Detect Turkish Offensive Tweets Pınar Arslan Independent Researcher 0707.pinar@gmail.com Abstract This paper describes a system (pin cod ) built for SemEval 2020 Task 12: OffensEval: Multi- lingual Offensive Language Identification in Social Media (Zampieri et al., 2020). I present the system based on the architecture of bidirectional long short-term memory networks (BiLSTM) concatenated with lexicon-based features and a social-network specific feature and then followed by two fully connected dense layers for detecting Turkish offensive tweets. The pin cod ’s system achieved a macro F1-score of 0.7496 for Sub-task A - Offensive language identification in Turkish. 1 Introduction With the appearance of influential social media platforms, offensive language is becoming more prevalent and visible. Harbouring behind a physically invisible author, bullies—either an intimate partner or an absolute stranger to victims— spread abusive, offensive and hateful messages against a particular person or a group of people through many internet platforms. The scope of the victims is broad and especially teenagers, women and immigrants are among targets for bullies. Online bullying leads to mental health issues including anxiety disorder, depression, it reduces self confidence, and causes lower academic achievement. Computational approaches to identify online bullying, hate speech, offensive and abusive language have gained acceleration and attention as a number of workshops have been organized for this purpose (Bosco et al., 2018; Fersini et al., 2018; Basile et al., 2019; Zampieri et al., 2019) towards generic users, women and/or immigrants in English, Italian, Spanish and German. SemEval 2020 Task 12 : OffensEval 2020 is probably the first workshop for identifying offensive language for Turkish language with Sub-task A - Offensive language identification (C¸ ¨oltekin, 2020). I participated in this sub-task the main goal of which is to segregate offensive posts from not offensive ones (i.e., OFF for offensive, NOT for not offensive class). Offensive posts comprise insults, threats, and untargeted profanity. Non-offensive posts do not contain any offense or profanity. The pin cod ’s system for this binary classification sub-task was based on BiLSTM networks incorporated with various lexicon-based features and the presence of user mentions (e.g. @username) and then followed by two fully connected dense layers. The scripts for preprocessing and the BiLSTM model can be found here1. The current study shows the features obtained from several lexicons and a social-network specific feature increased the performance of the BiLSTM model with a satisfactory F1-score of 0.7496. This paper is organized as follows: related work in section 2, system description in section 3, results in section 4, error analysis and discussion in section 5, and conclusions in section 6. 2 Related Work A spate of studies investigated hate speech in recent decades (Kwok and Wang, 2013; Burnap and Williams, 2015; Silva et al., 2016; Corazza et al., 2018a). The phenomenon of hate speech has been investigated This work is licensed under …

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