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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
587–593
Language:
URL:
https://www.aclweb.org/anthology/S18-1096
DOI:
10.18653/v1/S18-1096
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1096.pdf