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
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:
507–511
Language:
URL:
https://aclanthology.org/S18-1082
DOI:
10.18653/v1/S18-1082
Bibkey:
Cite (ACL):
Kevin Swanberg, Madiha Mirza, Ted Pedersen, and Zhenduo Wang. 2018. ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 507–511, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets (Swanberg et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1082.pdf