Sarcasm and Irony Detection in English Tweets

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 940)


This paper describes an approach to sarcasm and irony detection in English tweets. Accurate sarcasm and irony detection in text is crucial for numerous NLP applications like sentiment analysis, opinion mining and text summarization. The detection of irony and sarcasm in microblogging posts can be even more challenging because of the restricted length of the message at hand, the informal language, emoticons and hash tags used. In our approach we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were performed using supervised learning using different approaches for text preprocessing and feature extraction and four different classifiers. The corpus used was taken from SemEval2018 challenge containing a dataset with 3834 different tweets. The performance of the different approaches are reported and commented. The results have shown that the text preprocessing has very little impact on the results, while the word and sub-word frequencies are the most usable characteristics for determining irony in tweets. A separate experiment including a survey was also conducted in which human participants were challenged to label 20 given tweets from the dataset as ironic or not. The obtained results suggest that accurate irony detection in tweets can be a hard task even for humans.


Irony detection Sarcasm detection Tweets classification NLP 


  1. 1.
    Barbieri, F., Saggion, H.: Modelling irony in Twitter. In: Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 56–64. Association for Computational Linguistics (2014)Google Scholar
  2. 2.
    Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)Google Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011).
  4. 4.
    Filatova, E.: Irony and sarcasm: corpus generation and analysis using crowdsourcing. In: Chair, N.C.C., et al. (eds.) Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012). European Language Resources Association (ELRA), Istanbul, Turkey, May 2012Google Scholar
  5. 5.
    Giora, R.: On irony and negation. Discourse Process. 19(2), 239–264 (1995)CrossRefGoogle Scholar
  6. 6.
    González-Ibáñez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, HLT 2011, Association for Computational Linguistics, Stroudsburg, PA, USA, vol. 2, pp. 581–586 (2011)Google Scholar
  7. 7.
    Grice, H.P.: Logic and conversation. In: Cole, P., Morgan, J.L. (eds.) Syntax and Semantics, Speech Acts, vol. 3, pp. 41–58 (1978)Google Scholar
  8. 8.
    Hao, Y., Veale, T.: An ironic fist in a velvet glove: creative mis-representation in the construction of ironic similes. Minds Mach. 20(4), 635–650 (2010)CrossRefGoogle Scholar
  9. 9.
    Joshi, A., Bhattacharyya, P., Carman, M.J.: Automatic sarcasm detection: a survey. ACM Comput. Surv. 50(5), 73:1–73:22 (2017)CrossRefGoogle Scholar
  10. 10.
    Kaushik, S., Barot, P.M.: Sarcasm detection in sentiment analysis. Int. J. Adv. Res. Innov. Ideas Educ. 2(6), 1749–1758 (2016)Google Scholar
  11. 11.
    Liebrecht, C., Kunneman, F., Van den Bosch, A.: The perfect solution for detecting sarcasm in tweets #not. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 29–37. Association for Computational Linguistics (2013).
  12. 12.
    Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, ETMTNLP 2002. vol. 1, pp. 63–70. Association for Computational Linguistics (2002)Google Scholar
  13. 13.
    Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic tweets using deep convolutional neural networks. In: COLING (2016)Google Scholar
  14. 14.
    Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on Twitter: a behavioral modeling approach. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 97–106. WSDM 2015. ACM, New York (2015).
  15. 15.
    Sulis, E., Farias, D.I.H., Rosso, P., Patti, V., Ruffo, G.: Figurative messages and affect in Twitter: differences between # irony, #sarcasm and #not. Knowl.-Based Syst. 108, 132–143 (2016)CrossRefGoogle Scholar
  16. 16.
    Veale, T.: A computational exploration of creative smiles. In: Metaphor in Use: Context, Culture, and Communication, pp. 329–343. John Benjamins Publishing Company (2012)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringSs. Cyril and Methodius University in SkopjeSkopjeMacedonia

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