SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets
Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, Maite Martin, L. Alfonso Ureña-López
Abstract
Misogyny and xenophobia are some of the most important social problems. With the in- crease in the use of social media, this feeling ofhatred towards women and immigrants can be more easily expressed, therefore it can cause harmful effects on social media users. For this reason, it is important to develop systems ca- pable of detecting hateful comments automatically. In this paper, we describe our system to analyze the hate speech in English and Spanish tweets against Immigrants and Women as part of our participation in SemEval-2019 Task 5: hatEval. Our main contribution is the integration of three individual algorithms of predic- tion in a model based on Vote ensemble classifier.- Anthology ID:
- S19-2084
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 476–479
- Language:
- URL:
- https://aclanthology.org/S19-2084
- DOI:
- 10.18653/v1/S19-2084
- Cite (ACL):
- Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, Maite Martin, and L. Alfonso Ureña-López. 2019. SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 476–479, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets (Plaza-del-Arco et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/autopr/S19-2084.pdf
- Data
- HatEval