Abstract
Detecting sarcasm in text is a particularly challenging problem in computational semantics, and its solution may vary across different types of text. We analyze the performance of a domain-general sarcasm detection system on datasets from two very different domains: Twitter, and Amazon product reviews. We categorize the errors that we identify with each, and make recommendations for addressing these issues in NLP systems in the future.- Anthology ID:
- W18-1303
- Volume:
- Proceedings of the Workshop on Computational Semantics beyond Events and Roles
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Eduardo Blanco, Roser Morante
- Venue:
- SemBEaR
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 21–26
- Language:
- URL:
- https://aclanthology.org/W18-1303
- DOI:
- 10.18653/v1/W18-1303
- Cite (ACL):
- Natalie Parde and Rodney Nielsen. 2018. Detecting Sarcasm is Extremely Easy ;-). In Proceedings of the Workshop on Computational Semantics beyond Events and Roles, pages 21–26, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Sarcasm is Extremely Easy ;-) (Parde & Nielsen, SemBEaR 2018)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/W18-1303.pdf