We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter

Cynthia Van Hee, Els Lefever, Véronique Hoste


Abstract
Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.
Anthology ID:
J18-4010
Volume:
Computational Linguistics, Volume 44, Issue 4 - December 2018
Month:
December
Year:
2018
Address:
Cambridge, MA
Venue:
CL
SIG:
Publisher:
MIT Press
Note:
Pages:
793–832
Language:
URL:
https://aclanthology.org/J18-4010
DOI:
10.1162/coli_a_00337
Bibkey:
Cite (ACL):
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018. We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter. Computational Linguistics, 44(4):793–832.
Cite (Informal):
We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter (Van Hee et al., CL 2018)
Copy Citation:
PDF:
https://aclanthology.org/J18-4010.pdf