The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A multidimensional approach for detecting irony in Twitter
Open accessAbstract
Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or “tweets”. Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. “Toyota”) and user-generated tags (e.g. “#irony”). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.
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Cited by
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
Hate speech detection: A solved problem? The challenging case of long tail on Twitter
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SemEval-2018 Task 3: Irony Detection in English Tweets
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Methods for Coding Tobacco-Related Twitter Data: A Systematic Review
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Early detection of deception and aggressiveness using profile-based representations
Escalante, H. J., Villatoro-Tello, E., Garza, S. E., López-Monroy, A. P., Montes-y-Gómez, M., & Villaseñor-Pineda, L. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.07.040 · Cited by 44
Metadata
- Authors
- Reyes, A., Rosso, P., & Veale, T.
- Venue
- Language Resources and Evaluation
- Published
- 2012-07-24
- Volume
- 47
- Issue
- 1
- Pages
- 239-268
- ISSN
- 1574-020X (ISSN-L)
- 1574-0218
- OpenAlex
- W2041400887
- Cited by
- 454
- References
- 32
- FWCI
- 27.86
- Type
- journal article
- Language
- en
- Open access
- green
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