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        <title>Aggression Detection on Social Media Text Using Deep Neural Networks</title>
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            <title>Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)</title>
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            <namePart type="given">Vinodkumar</namePart>
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    <abstract>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 TRAC-1in 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%.</abstract>
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%0 Conference Proceedings
%T Aggression Detection on Social Media Text Using Deep Neural Networks
%A Singh, Vinay
%A Varshney, Aman
%A Akhtar, Syed Sarfaraz
%A Vijay, Deepanshu
%A Shrivastava, Manish
%Y Fišer, Darja
%Y Huang, Ruihong
%Y Prabhakaran, Vinodkumar
%Y Voigt, Rob
%Y Waseem, Zeerak
%Y Wernimont, Jacqueline
%S Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F singh-etal-2018-aggression
%X 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 TRAC-1in 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%.
%R 10.18653/v1/W18-5106
%U https://aclanthology.org/W18-5106
%U https://doi.org/10.18653/v1/W18-5106
%P 43-50

Markdown (Informal)

[Aggression Detection on Social Media Text Using Deep Neural Networks](https://aclanthology.org/W18-5106) (Singh et al., ALW 2018)

• Aggression Detection on Social Media Text Using Deep Neural Networks (Singh et al., ALW 2018)

ACL

• Vinay Singh, Aman Varshney, Syed Sarfaraz Akhtar, Deepanshu Vijay, and Manish Shrivastava. 2018. Aggression Detection on Social Media Text Using Deep Neural Networks. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 43–50, Brussels, Belgium. Association for Computational Linguistics.