Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets

Edward Dearden, Alistair Baron


Abstract
This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to distinguish between irony and non-irony in English tweets. We create a targeted feature set and analyse how different features are useful in the task of irony detection, achieving an F1-score of 0.5914. The analysis of individual features provides insight that may be useful in future attempts at detecting irony in tweets.
Anthology ID:
S18-1096
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
587–593
Language:
URL:
https://aclanthology.org/S18-1096
DOI:
10.18653/v1/S18-1096
Bibkey:
Cite (ACL):
Edward Dearden and Alistair Baron. 2018. Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 587–593, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets (Dearden & Baron, SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1096.pdf