@inproceedings{hayati-etal-2019-sunny,
title = "What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection",
author = "Hayati, Shirley Anugrah and
Chaudhary, Aditi and
Otani, Naoki and
Black, Alan W",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5527",
doi = "10.18653/v1/D19-5527",
pages = "212--216",
abstract = "Irony detection is an important task with applications in identification of online abuse and harassment. With the ubiquitous use of non-verbal cues such as emojis in social media, in this work we aim to study the role of these structures in irony detection. Since the existing irony detection datasets have {\textless}10{\%} ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.",
}
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<abstract>Irony detection is an important task with applications in identification of online abuse and harassment. With the ubiquitous use of non-verbal cues such as emojis in social media, in this work we aim to study the role of these structures in irony detection. Since the existing irony detection datasets have \textless10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.</abstract>
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%0 Conference Proceedings
%T What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection
%A Hayati, Shirley Anugrah
%A Chaudhary, Aditi
%A Otani, Naoki
%A Black, Alan W.
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hayati-etal-2019-sunny
%X Irony detection is an important task with applications in identification of online abuse and harassment. With the ubiquitous use of non-verbal cues such as emojis in social media, in this work we aim to study the role of these structures in irony detection. Since the existing irony detection datasets have \textless10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.
%R 10.18653/v1/D19-5527
%U https://aclanthology.org/D19-5527
%U https://doi.org/10.18653/v1/D19-5527
%P 212-216
Markdown (Informal)
[What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection](https://aclanthology.org/D19-5527) (Hayati et al., WNUT 2019)
ACL
- Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, and Alan W Black. 2019. What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 212–216, Hong Kong, China. Association for Computational Linguistics.