Irony Detection for Dutch: a Venture into the Implicit

Aaron Maladry, Els Lefever, Cynthia Van Hee, Veronique Hoste


Abstract
This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and and a transformer-based approach. In addition to building a baseline model, an important goal of this research is to explore the implementation of common-sense knowledge in the form of implicit sentiment, as we strongly believe that common-sense and connotative knowledge are essential to the identification of irony and implicit meaning in tweets.We show promising results and the presented approach can provide a solid baseline and serve as a staging ground to build on in future experiments for irony detection in Dutch.
Anthology ID:
2022.wassa-1.16
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
172–181
Language:
URL:
https://aclanthology.org/2022.wassa-1.16
DOI:
10.18653/v1/2022.wassa-1.16
Bibkey:
Cite (ACL):
Aaron Maladry, Els Lefever, Cynthia Van Hee, and Veronique Hoste. 2022. Irony Detection for Dutch: a Venture into the Implicit. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 172–181, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Irony Detection for Dutch: a Venture into the Implicit (Maladry et al., WASSA 2022)
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PDF:
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Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.wassa-1.16.mp4