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        <title>A K-Competitive Autoencoder for Aggression Detection in Social Media Text</title>
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        <namePart type="given">Promita</namePart>
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            <title>Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)</title>
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            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
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            <namePart type="given">Atul</namePart>
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            <namePart type="family">Ojha</namePart>
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    <abstract>We present an approach to detect aggression from social media text in this work. A winner-takes-all autoencoder, called Emoti-KATE is proposed for this purpose. Using a log-normalized, weighted word-count vector at input dimensions, the autoencoder simulates a competition between neurons in the hidden layer to minimize the reconstruction loss between the input and final output layers. We have evaluated the performance of our system on the datasets provided by the organizers of TRAC workshop, 2018. Using the encoding generated by Emoti-KATE, a 3-way classification is performed for every social media text in the dataset. Each data point is classified as ‘Overtly Aggressive’, ‘Covertly Aggressive’ or ‘Non-aggressive’. Results show that our (team name: PMRS) proposed method is able to achieve promising results on some of these datasets. In this paper, we have described the effects of introducing an winner-takes-all autoencoder for the task of aggression detection, reported its performance on four different datasets, analyzed some of its limitations and how to improve its performance in future works.</abstract>
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%0 Conference Proceedings
%T A K-Competitive Autoencoder for Aggression Detection in Social Media Text
%A Maitra, Promita
%A Sarkhel, Ritesh
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%S Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F maitra-sarkhel-2018-k
%X We present an approach to detect aggression from social media text in this work. A winner-takes-all autoencoder, called Emoti-KATE is proposed for this purpose. Using a log-normalized, weighted word-count vector at input dimensions, the autoencoder simulates a competition between neurons in the hidden layer to minimize the reconstruction loss between the input and final output layers. We have evaluated the performance of our system on the datasets provided by the organizers of TRAC workshop, 2018. Using the encoding generated by Emoti-KATE, a 3-way classification is performed for every social media text in the dataset. Each data point is classified as ‘Overtly Aggressive’, ‘Covertly Aggressive’ or ‘Non-aggressive’. Results show that our (team name: PMRS) proposed method is able to achieve promising results on some of these datasets. In this paper, we have described the effects of introducing an winner-takes-all autoencoder for the task of aggression detection, reported its performance on four different datasets, analyzed some of its limitations and how to improve its performance in future works.
%U https://aclanthology.org/W18-4410
%P 80-89

Markdown (Informal)

[A K-Competitive Autoencoder for Aggression Detection in Social Media Text](https://aclanthology.org/W18-4410) (Maitra & Sarkhel, TRAC 2018)

• A K-Competitive Autoencoder for Aggression Detection in Social Media Text (Maitra & Sarkhel, TRAC 2018)

ACL

• Promita Maitra and Ritesh Sarkhel. 2018. A K-Competitive Autoencoder for Aggression Detection in Social Media Text. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 80–89, Santa Fe, New Mexico, USA. Association for Computational Linguistics.