Artificial Intelligence Review

, Volume 43, Issue 4, pp 467–483 | Cite as

Computational irony: A survey and new perspectives

  • Byron C. Wallace


Irony is a fundamental rhetorical device. It is a uniquely human mode of communication, curious in that the speaker says something other than what he or she intends. Recently, computationally detecting irony has attracted attention from the natural language processing (NLP) and machine learning (ML) communities. While some progress has been made toward this end, I argue that current machine learning methods rely too heavily on shallow, unstructured, syntactic modeling of text to consistently discern ironic intent. Irony detection is an interesting machine learning problem because, in contrast to most text classification tasks, it requires a semantics that cannot be inferred directly from word counts over documents alone. To support this position, I survey the large body of existing philosophical/literary work investigating ironic communication. I then survey more recent computational efforts to operationalize irony detection in the fields of NLP and ML. I identify the disparities of the latter with respect to the former. Specifically, I highlight a major conceptual problem in all existing computational models of irony: none maintain an explicit model of the speaker/environment. I argue that without such an internal model of the speaker, irony detection is hopeless, as this model is necessary to represent expectations, which play a key role in ironic communication. I sketch possible means of embedding such models into computational approaches to irony detection. In particular, I introduce the pragmatic context model, which looks to operationalize computationally existing theories of irony. This work is a step toward unifying work on irony from literary, empirical and philosophical perspectives with modern computational models.


Irony Representation Machine learning 


  1. Attardo S (2000) Irony as relevant inappropriateness. J Pragmat 32(6):793–826CrossRefGoogle Scholar
  2. Bethard S, Yu H, Thornton A, Hatzivassiloglou V, Jurafsky D (2004) Automatic extraction of opinion propositions and their holders. In: 2004 AAAI spring symposium on exploring attitude and affect in text, p 2224Google Scholar
  3. Booth W (1975) A rhetoric of irony. University of Chicago Press, ILGoogle Scholar
  4. Burfoot C, Baldwin T (2009) Automatic satire detection: are you having a laugh? In: Proceedings of the ACL-IJCNLP 2009 conference short papers, pp 161–164. Association for Computational Linguistics (2009)Google Scholar
  5. Carvalho P, Sarmento L, Silva M, de Oliveira E (2009) Clues for detecting irony in user-generated contents: oh...!! it’s so easy;-) pp 53–56Google Scholar
  6. Clark H, Gerrig R (1984) On the pretense theory of irony. J Exp Psychol 113:121–126CrossRefGoogle Scholar
  7. Colebrook C (2004) Irony. RoutledgeGoogle Scholar
  8. Colston H (1997) Salting a wound or sugaring a pill: the pragmatic functions of ironic criticism. Discourse Process 23(1):25–45CrossRefGoogle Scholar
  9. Colston H (2001) On necessary conditions for verbal irony comprehension. Pragmatics & \(\#\) 38. Cognition 8(2):277–324Google Scholar
  10. Davey A (1978) Discourse production: a computer model of some aspects of a speaker. Edinburgh University PressGoogle Scholar
  11. Davidov D, Rappoport A (2006) Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words, pp 297–304Google Scholar
  12. Davidov D, Tsur O, Rappoport A (2010) Semi-supervised recognition of sarcastic sentences in twitter and amazon. Conference on natural language learning (CoNLL) p 107Google Scholar
  13. Greene E, Bodrumlu T, Knight K (2010) Automatic analysis of rhythmic poetry with applications to generation and translation. In: Proceedings of the 2010 conference on empirical methods in natural language processing, pp 524–533. Association for computational linguisticsGoogle Scholar
  14. Grice H (1975) Logic and conversation. pp 41–58Google Scholar
  15. Grice H (1978) Further notes on logic and conversation pp 113–127Google Scholar
  16. Guerra P, Veloso A, Meira Jr W, Almeida V (2011) From bias to opinion: a transfer-learning approach to real-time sentiment analysis. KDDGoogle Scholar
  17. Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8–12CrossRefGoogle Scholar
  18. Hao Y, Veale T (2010) An ironic fist in a velvet glove: creative mis-representation in the construction of ironic similes. Minds and Machines pp 1–16Google Scholar
  19. Hogg T (2010) Inferring preference correlations from social networks. Electron Commer Res Appl 9(1):29–37CrossRefGoogle Scholar
  20. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Machine Learning: ECML-98 pp 137–142Google Scholar
  21. Kanayama H, Nasukawa T (2006) Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the 2006 conference on empirical methods in natural language processing, pp 355–363. Association for computational linguisticsGoogle Scholar
  22. Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22(2):110–125CrossRefMathSciNetGoogle Scholar
  23. Kumon-Nakamura S, Glucksberg S (1995) How about another piece of pie: the allusional pretense theory of discourse irony. J Exp Psychol Gen 124:3–21CrossRefGoogle Scholar
  24. Lewis D (1998) Naive (Bayes) at forty: the independence assumption in information retrieval. Machine Learning: ECML-98 pp 4–15Google Scholar
  25. Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics, p 271. Association for computational linguisticsGoogle Scholar
  26. Pexman P, Glenwright M (2007) How do typically developing children grasp the meaning of verbal irony? J Neurolinguistics 20(2):178–196CrossRefGoogle Scholar
  27. Puckette M (1996) Pure data: another integrated computer music environment. In: Proceedings of the second intercollege computer music concerts pp 37–41Google Scholar
  28. Reiter E, Dale R (2000) Building natural language generation systemsGoogle Scholar
  29. Schaffer R (1982) Vocal cues for irony in english. Diss, (unveroff.), The Ohio State UniversityGoogle Scholar
  30. Scharrer L, Christmann U (2011) Voice modulations in german ironic speech. Lang Speech 54(4):435–465CrossRefGoogle Scholar
  31. Sindhwani V, Melville P (2009) Document-word co-regularization for semi-supervised sentiment analysis. In: Data Mining, 2008. ICDM’08. Eighth IEEE international conference on, pp 1025–1030. IEEEGoogle Scholar
  32. Sperber D, Wilson D (1981) Irony and the use-mention distinctionGoogle Scholar
  33. Swift J (1955) A modest proposal for preventing the children of poor people in ireland from being a burden to their parents or country; and for making them beneficial to the public (1729). Irish Tracts pp 1728–1733Google Scholar
  34. Tepperman J, Traum D, Narayanan S (2006) ”Yeah Right”: sarcasm recognition for spoken dialogue systemsGoogle Scholar
  35. Utsumi A (1996) A unified theory of irony and its computational formalization pp 962–967Google Scholar
  36. Utsumi A (2000) Verbal irony as implicit display of ironic environment: distinguishing ironic utterances from nonirony. J Pragmat 32(12):1777–1806CrossRefGoogle Scholar
  37. Wilson D, Sperber D (1992) On verbal irony. Lingua 87(1–2):53–76CrossRefGoogle Scholar
  38. Yi J, Nasukawa T, Bunescu R, Niblack W (2003) Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: ICDM, pp 427–434. IEEEGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Center for Evidence-Based MedicineBrown UniversityProvidenceUSA

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