@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T ArSarcasMoji Dataset: The Emoji Sentiment Roles in Arabic Ironic Contexts
%A Hakami, Shatha Ali A.
%A Hendley, Robert
%A Smith, Phillip
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F hakami-etal-2023-arsarcasmoji
%X 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.
%R 10.18653/v1/2023.arabicnlp-1.18
%U https://aclanthology.org/2023.arabicnlp-1.18
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.18
%P 208-217
Markdown (Informal)
[ArSarcasMoji Dataset: The Emoji Sentiment Roles in Arabic Ironic Contexts](https://aclanthology.org/2023.arabicnlp-1.18) (Hakami et al., ArabicNLP-WS 2023)
ACL