Aggression Detection on Social Media Text Using Deep Neural Networks

2018-10-01 · WS 2018 10 · Vinay Singh, Aman Varshney, Syed Sarfaraz Akhtar, Deepanshu Vijay, Manish Shrivastava

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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SilentFlame/AggressionDetection 공식 구현

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Classification General Classification

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