SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media

Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, Maite Martin, L. Alfonso Ureña-López


Abstract
Offensive language has an impact across society. The use of social media has aggravated this issue among online users, causing suicides in the worst cases. For this reason, it is important to develop systems capable of identifying and detecting offensive language in text automatically. In this paper, we developed a system to classify offensive tweets as part of our participation in SemEval-2019 Task 6: OffensEval. Our main contribution is the integration of lexical features in the classification using the SVM algorithm.
Anthology ID:
S19-2129
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
*SEMEVAL
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
735–738
Language:
URL:
https://www.aclweb.org/anthology/S19-2129
DOI:
10.18653/v1/S19-2129
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PDF:
http://aclanthology.lst.uni-saarland.de/S19-2129.pdf