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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.wassa-1.16.pdf