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
- 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:
- 587–593
- Language:
- URL:
- https://aclanthology.org/S18-1096
- DOI:
- 10.18653/v1/S18-1096
- Cite (ACL):
- Edward Dearden and Alistair Baron. 2018. Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 587–593, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets (Dearden & Baron, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1096.pdf