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Wide usage of social media platforms has increased the risk of aggression,which results in mental stress and affects the lives of people negatively likepsychological agony, fighting behavior, and disrespect to others. Majority ofsuch conversations contains code-mixed languages[28]. Additionally, the wayused to express thought or communication style also changes from one socialmedia plat-form to another platform (e.g., communication styles are differentin twitter and Facebook). These all have increased the complexity of theproblem. To solve these problems, we have introduced a unified and robustmulti-modal deep learning architecture which works for English code-mixeddataset and uni-lingual English dataset both.The devised system, usespsycho-linguistic features and very ba-sic linguistic features. Our multi-modaldeep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, andDisconnected RNN(with Glove and FastText embedding, both). Finally, the systemtakes the decision based on model averaging. We evaluated our system on EnglishCode-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained fromKaggle. Experimental results show that our proposed system outperforms all theprevious approaches on English code-mixed dataset and uni-lingual Englishdataset.

A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts