Advertisement

A Large Scale Study for Identification of Sarcasm in Textual Data

  • Pulkit MehndirattaEmail author
  • Devpriya Soni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 922)

Abstract

With the increase in the penetration of the Internet and the widespread acceptability of social networking sites, more people are coming forward to express their views and opinion about various topics. This has given a huge boost to the textual and multimedia content generated by these websites, giving opportunities to researchers and analysts to nd and generate patterns from this data. The problem of identification of sarcasm in textual data is quite challenging due to lack of annotation, intonation and facial expression. Big companies are spending millions in finding out, whether people were praising or mocking about their product, they can get the idea about the market trends and needs. Law enforcement agencies may also get benefit from this as they would be able to distinguish legitimate threats from exaggerations on the online social networks. A data-driven approach based on the neural networks and the concepts of deep learning has been evaluated using a blend of deep convolutional networks (CNN) and long short term memory (LSTM). The technique has been applied to the Self-Annotated Reddit Corpus (SARC) (http://nlp.cs.princeton.edu/SARC/), a large corpus for sarcasm research. The technique for domain specific and general data is also probed, as given in the dataset so as to check the accuracy of the proposed method. It has been observed that blending of the models further improves the accuracy of simple CNN model, and yields a more computationally efficient model of accuracy compared to standalone models. Our method has achieved an overall average precision of 73%.

Keywords

Online social networks Opinion mining and sentiment analysis Text analysis Irony and sarcasm Deep learning 

References

  1. 1.
    Tan, W., Blake, M.B., Saleh, I., Dustdar, S.: Social-network-sourced big data analytics. IEEE Internet Comput. 17(5), 62–69 (2013)CrossRefGoogle Scholar
  2. 2.
    Gastelum, Z.N., Whattam, K.M.: State-of-the-Art of Social Media Analytics Research. Pacific Northwest National Laboratory, pp. 1–9 (2013)Google Scholar
  3. 3.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in Twitter streaming data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS (LNAI), vol. 6332, pp. 1–15. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16184-1_1CrossRefGoogle Scholar
  4. 4.
    Tufekci, Z.: Big questions for social media big data: representativeness, validity and other methodological pitfalls. In: 2014 ICWSM, pp. 505–514 (2014)Google Scholar
  5. 5.
    Tepperman, J., Traum, D., Narayanan, S.: “Yeah Right”: sarcasm recognition for spoken dialogue systems. In: Ninth International Conference on Spoken Language Processing (2006)Google Scholar
  6. 6.
    Giora, R.: On irony and negation. Discourse Processes 19(2), 239–264 (1995)CrossRefGoogle Scholar
  7. 7.
    Brown, R.L.: The pragmatics of verbal irony. Language use and the uses of language, pp. 111–127 (1980)Google Scholar
  8. 8.
    Hamamoto, H.: Irony from a Cognitive Perspective. Pragmatics and Beyond New Series, pp. 257–270 (1998)Google Scholar
  9. 9.
    Attardo, S.: Irony as relevant inappropriateness. J. Pragmat. 32(6), 793–826 (2000)CrossRefGoogle Scholar
  10. 10.
    Partington, A.: Irony and reversal of evaluation. J. Pragmat. 39(9), 1547–1569 (2007)CrossRefGoogle Scholar
  11. 11.
    Sperber, D., Wilson, D.: Prcis of relevance: communication and cognition. Behav. Brain Sci. 10(4), 697–710 (1987)CrossRefGoogle Scholar
  12. 12.
    Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., Huang, R.: Sarcasm as contrast between a positive sentiment and negative situation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 704–714 (2013)Google Scholar
  13. 13.
    Gonzalez-Ibanez, 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-Volume 2, pp. 581–586. Association for Computational Linguistics, June 2011Google Scholar
  14. 14.
    Bharti, S.K., Babu, K.S., Jena, S.K.: Parsing-based sarcasm sentiment recognition in Twitter data. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 1373–1380. ACM, August 2015Google Scholar
  15. 15.
    Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 107–116. Association for Computational Linguistics, July 2010Google Scholar
  16. 16.
    Barbieri, F., Saggion, H., Ronzano, F.: Modelling sarcasm in Twitter, a novel approach. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 50–58 (2014)Google Scholar
  17. 17.
    Carvalho, P., Sarmento, L., Silva, M.J., De Oliveira, E.: Clues for detecting irony in user-generated contents: oh…!! it’s so easy;-. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, pp. 53–56. ACM, November 2009Google Scholar
  18. 18.
    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. ACM, February 2015Google Scholar
  19. 19.
    Utsumi, A.: Verbal irony as implicit display of ironic environment: distinguishing ironic utterances from nonirony. J. Pragmat. 32(12), 1777–1806 (2000)CrossRefGoogle Scholar
  20. 20.
    Kreuz, R.J., Caucci, G.M.: Lexical influences on the perception of sarcasm. In: Proceedings of the Workshop on Computational Approaches to Figurative Language, pp. 1–4. Association for Computational Linguistics, April 2007Google Scholar
  21. 21.
    Mehndiratta, P., Sachdeva, S., Soni, D.: Detection of sarcasm in text data using deep convolutional neural networks. Scalable Comput.: Pract. Exp. 18(3), 219–228 (2017)CrossRefGoogle Scholar
  22. 22.
    Mishra, A., Kanojia, D., Nagar, S., Dey, K., Bhattacharyya, P.: Harnessing cognitive features for sarcasm detection (2017). arXiv preprint arXiv:1701.05574
  23. 23.
    Kim, Y.: Convolutional neural networks for sentence classification (2014). arXiv preprint arXiv:1408.5882
  24. 24.
    Bamman, D., Smith, N.A.: Contextualized sarcasm detection on Twitter. In: ICWSM, pp. 2, 15 (2015)Google Scholar
  25. 25.
    Khodak, M., Saunshi, N., Vodrahalli, K.: A large self-annotated corpus for sarcasm (2017). arXiv preprint arXiv:1704.05579
  26. 26.
    Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  27. 27.
    Sosa, P.M.: Twitter Sentiment Analysis using combined LSTM-CNN Models (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

Personalised recommendations