Zeyad at SemEval-2019 Task 6: That’s Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in Tweets.

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 823–828 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 823 Zeyad at SemEval-2019 Task 6: That’s Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in Tweets Zeyad El-Zanaty Faculty of Engineering Alexandria University zeyadzanaty@gmail.com Abstract The objective of this paper is to provide a description for a classification system built for SemEval-2019 Task 6: OffensEval. This system classifies a tweet as either offensive or not offensive (Sub-task A) and further classifies offensive tweets into categories (Sub-tasks B - C). The system consists of two phases; a brute-force grid search to find the best learners amongst a given set and an ensemble of a subset of these best learners. The system achieved an F1-score of 0.728, ranking in subtask A, an F1-score score of 0.616 in subtask B and an F1-score of 0.509 in subtask C. 1 Introduction In OffensEval we break down offensive content into three sub-tasks taking the type and target of offenses into account. Sub-task A - Offensive lan- guage identification; In this sub-task we are inter- ested in the identification of offensive posts and posts containing any form of (untargeted) profan- ity. In this sub-task there are 2 categories in which the tweet could be classified Not Offensive - This post does not contain offense or profanity. Non- offensive posts do not include any form of offense or profanity. Sub-task B - Automatic categoriza- tion of offense types; In this sub-task we are inter- ested in categorizing offenses. Tweets are labeled from one of the following categories Targeted In- sult - A post containing an insult or a threat to an individual, group, or others; Untargeted - A post containing non-targeted profanity and swearing. Posts containing general profanity are not targeted but they contain non-acceptable language. On the other hand, insults and threats are targeted at an individual or group. Sub-task C - Offense target identification. Finally, in sub-task C we are inter- ested in the target of offenses. Only posts which are either insults or threats are included in this sub- task. The three categories included in sub-task C are the following: Individual - The target of the offensive post is an individual: a famous person, named individual or an unnamed person interact- ing in the conversation. Group - The target of the offensive post is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or something else. Other The target of the offen- sive post does not belong to any of the previous two categories (e.g. an organization, a situation, an event, or an issue).(Zampieri et al., 2019b) To work with such complicated tasks our ap- proach is an exhaustive one. We try combinations of many techniques in pre-processing, feature extraction and classification while tuning their hyper-parameters to find the best models with the leading F1-scores. With the gained information an ensemble of the top …

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