@inproceedings{datta-etal-2020-spyder,
title = "{S}pyder: Aggression Detection on Multilingual Tweets",
author = "Datta, Anisha and
Si, Shukrity and
Chakraborty, Urbi and
Naskar, Sudip Kumar",
editor = "Kumar, Ritesh and
Ojha, Atul Kr. and
Lahiri, Bornini and
Zampieri, Marcos and
Malmasi, Shervin and
Murdock, Vanessa and
Kadar, Daniel",
booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.trac-1.14",
pages = "87--92",
abstract = "In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (\textbf{Team name:} \textbf{Spyder}) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages {--} English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories {--} {``}Overtly Aggressive{''} (OAG), {``}Covertly Aggressive{''} (CAG) and {``}Non-Aggressive{''} (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10{\%}, 59.45{\%} and 44.84{\%} respectively for English, Hindi and Bengali.",
language = "English",
ISBN = "979-10-95546-56-6",
}
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<abstract>In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (Team name: Spyder) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages – English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories – “Overtly Aggressive” (OAG), “Covertly Aggressive” (CAG) and “Non-Aggressive” (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10%, 59.45% and 44.84% respectively for English, Hindi and Bengali.</abstract>
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%0 Conference Proceedings
%T Spyder: Aggression Detection on Multilingual Tweets
%A Datta, Anisha
%A Si, Shukrity
%A Chakraborty, Urbi
%A Naskar, Sudip Kumar
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Lahiri, Bornini
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%Y Murdock, Vanessa
%Y Kadar, Daniel
%S Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-56-6
%G English
%F datta-etal-2020-spyder
%X In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (Team name: Spyder) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages – English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories – “Overtly Aggressive” (OAG), “Covertly Aggressive” (CAG) and “Non-Aggressive” (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10%, 59.45% and 44.84% respectively for English, Hindi and Bengali.
%U https://aclanthology.org/2020.trac-1.14
%P 87-92
Markdown (Informal)
[Spyder: Aggression Detection on Multilingual Tweets](https://aclanthology.org/2020.trac-1.14) (Datta et al., TRAC 2020)
ACL
- Anisha Datta, Shukrity Si, Urbi Chakraborty, and Sudip Kumar Naskar. 2020. Spyder: Aggression Detection on Multilingual Tweets. In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pages 87–92, Marseille, France. European Language Resources Association (ELRA).