Abstract
In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020, namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.
Cite
CITATION STYLE
Kurniawan, S., Budi, I., & Ibrohim, M. O. (2020). IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language Identification. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 1998–2005). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.263
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