Sarcasm Detection Using Incongruity Within Target Text

  • Aditya JoshiEmail author
  • Pushpak Bhattacharyya
  • Mark J. Carman
Part of the Cognitive Systems Monographs book series (COSMOS, volume 37)


Prior work in sarcasm detection uses indicators such as (a) unigrams and pragmatic features (such as emoticons, etc.) by González-Ibánez et al. (Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies: short papers-volume 2, pp 581–586, 2011), Carvalho et al. (Proceedings of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion. ACM, pp 53–56, 2009), Barbieri et al. (Modelling sarcasm in twitter: a novel approach, ACL 2014, p 50, 2014b), or (b) patterns extracted from techniques such as hashtag-based sentiment by Maynard and Greenwood (Proceedings of LREC, 2014), Liebrecht et al. (The perfect solution for detecting sarcasm in tweets# not, 2013), a positive verb being followed by a negative situation by Riloff et al. (Proceedings of the conference on empirical methods in natural language processing 2013, pp 704–714, 2013), or discriminative n-grams by Tsur et al. (ICWSM, 2010), Davidov et al. (Proceedings of the fourteenth conference on computational natural language learning. Association for Computational Linguistics, pp 107–116, 2010b).

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Aditya Joshi
    • 1
    Email author
  • Pushpak Bhattacharyya
    • 2
  • Mark J. Carman
    • 3
  1. 1.IITB-Monash Research AcademyIndian Institute of Technology BombayMumbaiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology BombayMumbaiIndia
  3. 3.Faculty of Information TechnologyMonash UniversityMelbourneAustralia

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