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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Publication date6/06/2018
Host publicationProceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018)
PublisherAssociation for Computational Linguistics
Number of pages7
<mark>Original language</mark>English


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.