Abstract
Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66%. When explicit hashtag information like #irony is included in the data, the system even obtains an F1-score of 92.77%. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).- Anthology ID:
- C16-1257
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2730–2739
- Language:
- URL:
- https://aclanthology.org/C16-1257
- DOI:
- Cite (ACL):
- Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2016. Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2730–2739, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets (Van Hee et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/C16-1257.pdf