What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection

Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W Black


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 <10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.
Anthology ID:
D19-5527
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–216
Language:
URL:
https://www.aclweb.org/anthology/D19-5527
DOI:
10.18653/v1/D19-5527
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5527.pdf