ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets

Kevin Swanberg, Madiha Mirza, Ted Pedersen, Zhenduo Wang


Abstract
This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.
Anthology ID:
S18-1082
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:
507–511
Language:
URL:
https://www.aclweb.org/anthology/S18-1082
DOI:
10.18653/v1/S18-1082
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1082.pdf