Sarcasm Detection Using Features Based on Indicator and Roles

  • Satoshi HiaiEmail author
  • Kazutaka Shimada
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)


Sarcasm is a non-literalistic expression and presents a negative meaning with positive expressions. Sarcasm detection is a significant challenge for sentiment analysis which is to analyze documents with opinions. In this study, we propose a method of sarcasm detection on Twitter. We focus on two kinds of feature words. One is words modified by the indicator “
”. The other is words expressing a role. First, we extract these words from tweets. Next, our method uses the lists of these words for a machine learning approach to detect sarcastic tweets. The lists of extracted words are used as features in our method. In the experiment, we compare our method with a baseline based on the features in previous studies. The experimental result shows the effectiveness of our method.


Sarcasm Sentiment analysis Opinion mining Microblogging Classification 



This work was partially supported by JSPS KAKENHI Grant Number 17H01840.


  1. 1.
    Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., Reyes, A.: Semeval-2015 task 11: sentiment analysis of figurative language in twitter. In: Proceedings 9th International Workshop on Semantic Evaluation (SemEval2015), Co-located with NAACL, pp. 470–478 (2015)Google Scholar
  2. 2.
    Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)CrossRefGoogle Scholar
  3. 3.
    Joshi, A., Sharma, V., Bhattacharyya, P.: Harnessing context incongruity for sarcasm detection. In: Proceedings of ACL-IJCNLP, pp. 757–762 (2015)Google Scholar
  4. 4.
    Karoui, J., Farah, B., Moriceau, V., Aussenac-Gilles, N., Hadrich-Belguith, L.: Towards a contextual pragmatic model to detect irony in tweets. In: Proceedings of ACL-IJCNLP, pp. 644–650 (2015)Google Scholar
  5. 5.
    Riloff, E., Qadir, A., Surve, P., Silva, L.D., Gilbert, N., Huang, R.: Sarcasm as contrast between a positive sentiment and negative situation. In: Proceedings of EMNLP 2013, pp. 704–714 (2013)Google Scholar
  6. 6.
    Campbell, J.D., Katz, A.N.: Are there necessary conditions for inducing a sense of sarcastic irony? Lang. Resour. Eval. 49(6), 459–480 (2012)Google Scholar
  7. 7.
    Hernndez-Farias, I., Benedi, J.M., Rosso, P.: Applying basic features from sentiment analysis on automatic irony detection. In: Proceedings of 7th ibPRIA, pp. 337–344 (2015)Google Scholar
  8. 8.
    Tungthamthiti, P., Shirai, K., Mohd, M.: Recognition of sarcasm in tweets based on concept level sentiment analysis and supervised learning approaches. In: Proceedings of the 28th PACLIC, pp. 404–413 (2014)Google Scholar
  9. 9.
    Kobayashi, N., Inui, K., Matsumoto, Y., Tateishi, K., Fukushima, T.: Collecting evaluative expressions for opinion extraction. In: Proceedings of IJCNLP-04, pp. 584–589 (2004)Google Scholar
  10. 10.
    Higashiyama, M., Inui, K., Matsumoto, Y.: Acquiring noun polarity knowledge using selectional preferences. In: Proceedings of the 14th Annual Meeting of the Association for NLP, pp. 584–587 (2008)Google Scholar
  11. 11.
    Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of html documents. In: Proceedings of EMNLP-CoNLL, pp. 1075–1083 (2007)Google Scholar
  12. 12.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Artificial IntelligenceKyushu Institute of TechnologyFukuokaJapan

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