Topic-Enriched Word Embeddings for Sarcasm Identification

  • Aytuğ OnanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 984)


Sarcasm is a type of nonliteral language, where people may express their negative sentiments with the use of words with positive literal meaning, and, conversely, negative meaning words may be utilized to indicate positive sentiment. User-generated text messages on social platforms may contain sarcasm. Sarcastic utterance may change the sentiment orientation of text documents from positive to negative, or vice versa. Hence, the predictive performance of sentiment classification schemes may be degraded if sarcasm cannot be properly handled. In this paper, we present a deep learning based approach to sarcasm identification. In this regard, the predictive performance of topic-enriched word embedding scheme has been compared to conventional word-embedding schemes (such as, word2vec, fastText and GloVe). In addition to word-embedding based feature sets, conventional lexical, pragmatic, implicit incongruity and explicit incongruity based feature sets are considered. In the experimental analysis, six subsets of Twitter messages have been taken into account, ranging from 5000 to 30.000. The experimental analysis indicate that topic-enriched word embedding schemes utilized in conjunction with conventional feature sets can yield promising results for sarcasm identification.


Sarcasm detection Word-embedding based features Deep learning 


  1. 1.
    Joshi, A., Bhattacharyya, P., Carman, M.J.: Automatic sarcasm detection: a survey. ACM Comput. Surv. 50, 73 (2017)CrossRefGoogle Scholar
  2. 2.
    Fersini, E., Messina, E., Pozzi, F.A.: Sentiment analysis: Bayesian ensemble learning. Decis. Support Syst. 68, 26–38 (2014)CrossRefGoogle Scholar
  3. 3.
    Joshi, A., Bhattacharyya, P., Carman, M.J.: Understanding the phenomenon of sarcasm. In: Joshi, A., Bhattacharyya, P., Carman, M.J. (eds.) Investigations in Computational Sarcasm, pp. 33–57. Springer, Berlin (2018)CrossRefGoogle Scholar
  4. 4.
    Onan, A.: Sarcasm identification on twitter: a machine learning approach. In: Silhavy, R., Senkerik, R., Kominkova, Z., Prokopova, Z., Silhavy, P. (eds.) Artificial Intelligence Trends in Intelligent Systems, pp. 374–383. Springer, Berlin (2017)CrossRefGoogle Scholar
  5. 5.
    Muresan, S., Gonzalez-Ibanez, R., Ghosh, D., Wacholder, N.: Identification of nonliteral language in social media: a case study on sarcasm. J. Assoc. Inf. Sci. Technol. (2016). Scholar
  6. 6.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD Conference, pp. 56–65. ACM, New York (2007)Google Scholar
  7. 7.
    Zhang, M., Zhang, Y., Fu, G.: Tweet sarcasm detection using deep neural network. In: Proceedings of the 26th International Conference on Computational Linguistics, pp. 2449–2460. COLING, New York (2016)Google Scholar
  8. 8.
    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, pp. 581–586. ACL, New York (2011)Google Scholar
  9. 9.
    Reyes, A., Rosso, P., Buscaldi, D.: From humar recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)CrossRefGoogle Scholar
  10. 10.
    Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)CrossRefGoogle Scholar
  11. 11.
    Ptacek, T., Habernal, I., Hong, J.: Sarcasm detection on czech and english twitter. In: Proceedings of COLING 2014, pp. 213–223. COLING, New York (2014)Google Scholar
  12. 12.
    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. ACL, New York (2014)Google Scholar
  13. 13.
    Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on twitter: a behavioural modelling approach. In: Proceedings of the Eight ACM International Conference on Web Search and Data Mining, pp. 97–106. ACM, New York (2015)Google Scholar
  14. 14.
    Hernandez-Faria, D., Patti, V., Rosso, P.: Irony detection in twitter: the role of affective content. ACM Trans. Internet Technol. 16(3), 1–19 (2016)Google Scholar
  15. 15.
    Bouazizi, M., Ohtsuki, T.O.: A pattern-based approach for sarcasm detection on Twitter. IEEE Access 4, 5477–5488 (2016)CrossRefGoogle Scholar
  16. 16.
    Kumar, L., Somani, A., Bhattacharyya, P.: Having 2 hours to write a paper is fun: detecting sarcasm in numerical portions of text. arXiv preprint arXiv:1709.01950 (2017)
  17. 17.
    Mishra, A., Kanojia, D., Nagar, S., Dey, K., Bhattacharyya, P.: Harnessing cognitive features for sarcasm detection. arXiv preprint arXiv:1701.05574 (2017)
  18. 18.
    Ghosh, D., Guo, W., Muresan, S.: Sarcastic or not: word embeddings to predict the literal or sarcastic meaning of words. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1003–1012. ACL, New York (2015)Google Scholar
  19. 19.
    Joshi, A., Tripathi, V., Patel, K., Bhattacharyya, P., Carman, M.: Are word embedding-based features useful for sarcasm detection. arXiv preprint arXiv:1610.00883 (2016)
  20. 20.
    Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815 (2016)
  21. 21.
    Rezaeinia, S.M., Ghodsi, A., Rahmani, R.: Improving the accuracy of pre-trained word embeddings for sentiment analysis. arXiv preprint arXiv:1711.08609 (2017)
  22. 22.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  23. 23.
    Bairong, Z., Wenbo, W., Zhiyu, L., Chonghui, Z., Shinozaki, T.: Comparative analysis of word embedding methods for DSTC6 end-to-end conversation modelling track. In: Proceedings of the 6th Dialog System Technology Challenges Workshop (2017)Google Scholar
  24. 24.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
  25. 25.
    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, pp. 1532–1543. ACL, New York (2014)Google Scholar
  26. 26.
    Moody, C.E., Johnson, R., Zhang, T.: Mixing Dirichlet Topic Models and Word Embeddings to Make Lda2vec (2014).
  27. 27.
    Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058 (2014)
  28. 28.
    Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)CrossRefGoogle Scholar
  29. 29.
    Kilimci, Z., Akyokus, S.: Deep learning and word embedding-based heterogeneous classifier ensembles for text classification. Complexity 2018, 1–10 (2018)CrossRefGoogle Scholar
  30. 30.
    Cireşan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. arXiv preprint arXiv:1202.2745 (2012)
  31. 31.
    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 Computation Linguistics, pp. 581–586. ACL, New York (2011)Google Scholar
  32. 32.
    Paredes-Valverde, M.A., Colomo-Palacios, R., Salas-Zarate, M., Valencia-Garcia, R.: Sentiment analysis in Spanish for improvement of product and services: a deep learning approach. Sci. Program. 2017, 1–12 (2017)Google Scholar
  33. 33.
    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. ACL, New York (2013)Google Scholar
  34. 34.
    Ramteke, A., Malu, A., Bhattacharyya, P., Nath, J.S.: Detecting turnarounds in sentiment analysis: thwarting. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 860–865. ACL, New York (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of Engineering and Architectureİzmir Katip Çelebi UniversityİzmirTurkey

Personalised recommendations