@inproceedings{hakami-etal-2023-arsarcasmoji,
    title = "{A}r{S}arcas{M}oji Dataset: The Emoji Sentiment Roles in {A}rabic Ironic Contexts",
    author = "Hakami, Shatha Ali A.  and
      Hendley, Robert  and
      Smith, Phillip",
    editor = "Sawaf, Hassan  and
      El-Beltagy, Samhaa  and
      Zaghouani, Wajdi  and
      Magdy, Walid  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Habash, Nizar  and
      Khalifa, Salam  and
      Keleg, Amr  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      Mrini, Khalil  and
      Almatham, Rawan",
    booktitle = "Proceedings of ArabicNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.arabicnlp-1.18/",
    doi = "10.18653/v1/2023.arabicnlp-1.18",
    pages = "208--217",
    abstract = "In digital communication, emoji are essential in decoding nuances such as irony, sarcasm, and humour. However, their incorporation in Arabic natural language processing (NLP) has been cautious because of the perceived complexities of the Arabic language. This paper introduces ArSarcasMoji, a dataset of 24,630 emoji-augmented texts, with 17. 5{\%} that shows irony. Through our analysis, we highlight specific emoji patterns paired with sentiment roles that denote irony in Arabic texts. The research counters prevailing notions, emphasising the importance of emoji{'}s role in understanding Arabic textual irony, and addresses their potential for accurate irony detection in Arabic digital content."
}