Exploring the Realization of Irony in Twitter Data

Cynthia Van Hee, Els Lefever, Véronique Hoste


Abstract
Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.
Anthology ID:
L16-1283
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1794–1799
Language:
URL:
https://aclanthology.org/L16-1283
DOI:
Bibkey:
Cite (ACL):
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2016. Exploring the Realization of Irony in Twitter Data. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1794–1799, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Exploring the Realization of Irony in Twitter Data (Van Hee et al., LREC 2016)
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PDF:
https://aclanthology.org/L16-1283.pdf