A multidimensional approach for detecting irony in Twitter

• Published in: Language Resources and Evaluation Vol. 47; no. 1; pp. 239 - 268
• Main Authors: Reyes, Antonio, Rosso, Paolo, Veale, Tony
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01-03-2013; Springer Netherlands; Springer Nature B.V
• Subjects: Assessments; Automatic text analysis; Automotive engineering; Blogs; Classification; Computational Linguistics; Computer Science; Constructed languages; Content analysis; Content words; Data mining; Decision analysis; Decision making; Digital media; Emoticons; Feature recognition; Humor; Identification; Information processing; Intonation; Irony; Language and Literature; Linguistics; Meaning; Media; On-line systems; Online; Recognition; Sentiment analysis; Signatures; Social media; Social networks; Social Sciences; Special Issue: Analysis of Short Texts on the Web; Text messaging; Texts; Two dimensional models; Words; Figurative language processing; Negation; Irony detection; Web text analysis
• ISSN: 1574-020X; 1572-8412; 1574-0218
• DOI: 10.1007/s10579-012-9196-x

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.