Language Resources and Evaluation

, Volume 47, Issue 1, pp 239–268 | Cite as

A multidimensional approach for detecting irony in Twitter

Article

Abstract

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.

Keywords

Irony detection Figurative language processing Negation Web text analysis 

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Natural Language Engineering Lab, ELiRFUniversidad Politécnica de ValenciaValenciaSpain
  2. 2.School of Computer Science and InformaticsUniversity College DublinDublinIreland

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