Detecting Sarcasm is Extremely Easy ;-)

Natalie Parde, Rodney Nielsen


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
Venues:
NAACL | SemBEaR | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–26
Language:
URL:
https://aclanthology.org/W18-1303
DOI:
10.18653/v1/W18-1303
Bibkey:
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, 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-1303.pdf