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ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets
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Authors: Kevin Swanberg,Madiha Mirza,Ted Pedersen,Zhenduo Wang
Where published: SEMEVAL 2018 6
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/S18-1082/
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.
Where published: SEMEVAL 2018 6
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/S18-1082/
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.
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