Spyder: Aggression Detection on Multilingual Tweets
In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (\textbf{Team name:} \textbf{Spyder}) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages {--} English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories {--} {``}Overtly Aggressive{''} (OAG), {``}Covertly Aggressive{''} (CAG) and {``}Non-Aggressive{''} (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10{\%}, 59.45{\%} and 44.84{\%} respectively for English, Hindi and Bengali.
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