IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM

Anita Saroj, Supriya Chanda, Sukomal Pal

Abstract
This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro F1 score of 0.779 and 0.718 for Arabic and Greek, respectively.
Anthology ID:
2020.semeval-1.265
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
2012–2016
Language:
URL:
https://aclanthology.org/2020.semeval-1.265
DOI:
10.18653/v1/2020.semeval-1.265
Bibkey:
Cite (ACL):
Anita Saroj, Supriya Chanda, and Sukomal Pal. 2020. IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2012–2016, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
IRlab@IITV at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using SVM (Saroj et al., SemEval 2020)
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