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
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 212–216
- Language:
- URL:
- https://aclanthology.org/D19-5527
- DOI:
- 10.18653/v1/D19-5527
- Cite (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.
- Cite (Informal):
- What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (Hayati et al., WNUT 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5527.pdf