The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
From humor recognition to irony detection: The figurative language of social media
Open accessAbstract
The research described in this paper is focused on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused on describing a model for recognizing these phenomena in social media, such as ''tweets''. Our experiments are centered on five data sets retrieved from Twitter taking advantage of user-generated tags, such as ''#humor'' and ''#irony''. The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and figurative usages of language, are positive regarding humor, and encouraging regarding irony.
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Cites
Grice, H. P. (1975) · Speech Acts · doi:10.1163/9789004368811_003 · Cited by 23,221
Cited by
Adikari, A., de Silva, D., Moraliyage, H., Alahakoon, D., Wong, J., Gancarz, M., Chackochan, S., Park, B., Heo, R., & Leung, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.015 · Cited by 71
Kerr, H., Booth, R., & Jackson, K. (2020) · Journal of Medical Internet Research · doi:10.2196/16540 · Cited by 31
Metadata
- Authors
- Reyes, A., Rosso, P., & Buscaldi, D.
- Venue
- Data & Knowledge Engineering
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 74
- Pages
- 1-12
- ISSN
- 0169-023X (ISSN-L)
- OpenAlex
- W2038634595
- Cited by
- 377
- References
- 1
- FWCI
- 17.73
- Type
- journal article
- Language
- en
- Open access
- green
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