NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 2086–2089 Barcelona, Spain (Online), December 12, 2020. 2086 NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets Hamada A. Nayel Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University hamada.ali@fci.bu.edu.eg Abstract In this paper, we present the system submitted to “SemEval-2020 Task 12”. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20%, 81.82% f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17% and 44.51% f1-score on test set respectively. 1 Introduction The tremendous usage of social media platforms makes it important to apply different Natural Language Processing (NLP) tasks on these platforms. Different tasks, such as cyberbullying identification, hate speech detection, sarcasm detection and offensive language detection attracted NLP researchers to concentrate on automation of these tasks (Kwok and Wang, 2013). One of these tasks which gained a research interests is automatic offensive language detection. Offensive language is widespread in social media. Computational offensive language detection is a solution to identify such hostility and has shown promising performance (Nayel and L, 2019). Arabic is a significant language having an immense number of speakers as it is the official language of 22 countries (Guellil et al., 2019). It is recognized as the 4th most used language of the Internet (Boudad et al., 2018). The research in NLP for Arabic is constantly increasing (Nayel et al., 2019). Automatic offensive language detection becomes an important NLP task due to the overwhelming usage of social media. Automatic offensive language identification in Arabic is a challenge due to the complexity of Arabic language (Nayel, 2019). In this paper we describe the model that has been submitted to the offensive language detection shared task ”OffensEval 2020” (Zampieri et al., 2020). Given a tweet, then the task in brief is to determine whether it contains an offensive language or not. The first version, ”OffensEval 2019”, was held at SemEval 2019 (Zampieri et al., 2019b). A dataset containing English tweets and annotated using a hierarchical three-level annotation model has been used in ”OffensEval 2019” (Zampieri et al., 2019a). In ”OffensEval 2020”, in addition to English, four more languages have been added to the dataset namely, Arabic, Danish, Greek and Turkish. We participated in ”OffensEval 2020” for Arabic. A machine learning based approach has been used to develop our submission. Term Frequency/ Inverse Document Frequency (TF/IDF) vector space model has been used to represent the given tweets. 2 Related Work Recently, offensive language detection has gained significant attention and a lot of contributions have been recorded in this area (Waseem et al., 2017; Davidson et al., 2017; Kumar et al., 2018; Mubarak et al., 2017; Malmasi and Zampieri, 2018; Mandl et al., 2019). Zampieri et …

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