@inproceedings{kurniawan-etal-2020-ir3218,
    title = "{IR}3218-{UI} at {S}em{E}val-2020 Task 12: Emoji Effects on Offensive Language {I}dentifi{C}ation",
    author = "Kurniawan, Sandy  and
      Budi, Indra  and
      Ibrohim, Muhammad Okky",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.263/",
    doi = "10.18653/v1/2020.semeval-1.263",
    pages = "1998--2005",
    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."
}