Aggression Detection on Social Media Text Using Deep Neural Networks
In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by \textbf{TRAC-1}in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: {``}Overtly Aggressive{''}, {``}Covertly Aggressive{''} and {``}Non-Aggressive{''}. We experimented with 6 classification models and our CNN model on a 10 K-fold cross-validation gave the best result with the prediction accuracy of 73.2{\%}.
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