Exploring Author Context for Detecting Intended vs Perceived Sarcasm

Silviu Oprea, Walid Magdy


Abstract
We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.
Anthology ID:
P19-1275
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2854–2859
Language:
URL:
https://aclanthology.org/P19-1275
DOI:
10.18653/v1/P19-1275
Bibkey:
Cite (ACL):
Silviu Oprea and Walid Magdy. 2019. Exploring Author Context for Detecting Intended vs Perceived Sarcasm. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2854–2859, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (Oprea & Magdy, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1275.pdf
Video:
 https://vimeo.com/384744638