Different Approaches in Sarcasm Detection: A Survey

  • Rupali Amit BagateEmail author
  • R. Suguna
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)


Sarcasm is an unwelcome impact or a linguistic circumstance to express histrionic and bitterly opinions. In sarcasm single word in a sentence can flip the polarity of positive or negative statement totally. Therefore sarcasm occurs when there is an imbalance between text and context. This paper surveys different approaches and datasets for sarcasm detection. Different approaches surveyed are statistical approach, rule based approach, classification approach and deep learning approach. It also gives insight to different methodologies used in past for sarcasm detection. After surveying we found deep learning is generating a good result as compare to other approaches.


Sentiment analysis Sarcasm detection Machine learning Deep learning 


  1. 1.
    Abdi, A., Shamsuddin, S.M., Aliguliyev, R.M.: QMOS: query-based multi-documents opinion-oriented summarization. Inf. Process. Manag. 54(2), 318–338 (2018)CrossRefGoogle Scholar
  2. 2.
  3. 3.
  4. 4.
    Bhattacharyya, P., Carman, M.J., Joshi, A.: Automatic sarcasm detection: a survey. ACM Comput. Surv. (CSUR) 50(5), 22 (2017). Article No. 73Google Scholar
  5. 5.
    Chandankhede, C., Chaudhari, P.: Literature survey of sarcasm detection. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 22 March 2017Google Scholar
  6. 6.
    Yadav, S., Gupta, K., Rajput, B., Kumari, K., Tayal, D.: Polarity detection of sarcastic political tweets (2014)Google Scholar
  7. 7.
    Filatova, E.: Irony and sarcasm: corpus generation and analysis using crowdsourcing (2012)Google Scholar
  8. 8.
    Tsur, O., Rappoport, A., Davidov, D.: Semi-supervised recognition of sarcastic sentences in Twitter and Amazon (2010)Google Scholar
  9. 9.
    Tungthamthiti, P., Kiyoaki, S., Mohd, M.: Recognition of sarcasms in tweets based on concept level sentiment analysis and supervised learning approaches (2014)Google Scholar
  10. 10.
    Purwarianti, A., Lunando, E.: Indonesian social media sentiment analysis with sarcasm detection (2013)Google Scholar
  11. 11.
    Babu, K.S., Jena, S.K., Bharti, S.K.: Parsing-based sarcasm sentiment recognition in Twitter data (2015)Google Scholar
  12. 12.
    Zafarani, R., Liu, H., Rajadesingan, A.: Sarcasm detection on Twitter: a behavioral modeling approach (2015)Google Scholar
  13. 13.
    Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., Huang, R.: Sarcasm as contrast between a positive sentiment and negative situation (2013)Google Scholar
  14. 14.
    Barbieri, F., Saggion, H., Ronzano, F.: Modelling sarcasm in Twitter, a novel approach (2014)Google Scholar
  15. 15.
    Giachanou, A., Crestani, F.: Like it or not: a survey of Twitter sentiment analysis methods. ACM Comput. Surv. 49(2), 28 (2016)CrossRefGoogle Scholar
  16. 16.
    Pozzi, F.A., Messina, E., Fersini, E.: Detecting irony and sarcasm in microblogs: the role of expressive signals and ensemble classifiers (2015)Google Scholar
  17. 17.
    González-Ibánez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look, vol. 2. Association for Computational Linguistics (2011)Google Scholar
  18. 18.
    Bouazizi, M., Ohtsuki, T.: A pattern-based approach for sarcasm detection on Twitter (2016)CrossRefGoogle Scholar
  19. 19.
    Walker, M., Lukin, S.: Really? Well. Apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue (2013)Google Scholar
  20. 20.
    Liu, P., Chen, W., Ou, G., Wang, T., Yang, D., Lei, K.: Sarcasm detection in social media based on imbalanced classification. Springer, Cham (2014)CrossRefGoogle Scholar
  21. 21.
    Rappoport, A., Davidov, D.: Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words (2006)Google Scholar
  22. 22.
    Grice, H.P.: Logic and conversation. In: Speech Acts, vol. 3 (1975)Google Scholar
  23. 23.
    Camp, E.: Sarcasm, pretense, and the semantics/pragmatics distinction. Noûs 4, 587–634 (2012)CrossRefGoogle Scholar
  24. 24.
    Tripathi, V., Bhattacharyya, P., Carman, M., Joshi, A.: Harnessing sequence labeling for sarcasm detection in dialogue from TV series ‘Friends’ (2016)Google Scholar
  25. 25.
    Tepperman, J., Traum, D., Narayanan, S.: “Yeah Right”: sarcasm recognition for spoken dialogue systems (2006)Google Scholar
  26. 26.
    Cambria, E., Hazarika, D., Vij, P., Poria, S.: A deeper look into sarcastic tweets using deep convolutional neural networks, Osaka, Japan (2016)Google Scholar
  27. 27.
    Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews (2002)Google Scholar
  28. 28.
    Ghosh, A., Veale, T.: Fracking sarcasm using neural network (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennaiIndia
  2. 2.Army Institute of TechnologyPuneIndia

Personalised recommendations