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The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?

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Sentiment Analysis and Ontology Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 639))

Abstract

Due to the growing volume of available textual information, there is a great demand for Natural Language Processing (NLP) techniques that can automatically process and manage texts, supporting the information retrieval and communication in core areas of society (e.g. healthcare, business, and science). NLP techniques have to tackle the often ambiguous and linguistic structures that people use in everyday speech. As such, there are many issues that have to be considered, for instance slang, grammatical errors, regional dialects, figurative language , etc. Figurative Language (FL), such as irony , sarcasm , simile, and metaphor, poses a serious challenge to NLP systems. FL is a frequent phenomenon within human communication, occurring both in spoken and written discourse including books, websites, fora, chats, social network posts, news articles and product reviews. Indeed, knowing what people think can help companies, political parties, and other public entities in strategizing and decision-making polices. When people are engaged in an informal conversation, they almost inevitably use irony (or sarcasm) to express something else or different than stated by the literal sentence meaning. Sentiment analysis methods can be easily misled by the presence of words that have a strong polarity but are used sarcastically, which means that the opposite polarity was intended. Several efforts have been recently devoted to detect and tackle FL phenomena in social media. Many of applications rely on task-specific lexicons (e.g. dictionaries, word classifications) or Machine Learning algorithms. Increasingly, numerous companies have begun to leverage automated methods for inferring consumer sentiment from online reviews and other sources. A system capable of interpreting FL would be extremely beneficial to a wide range of practical NLP applications. In this sense, this chapter aims at evaluating how two specific domains of FL, sarcasm and irony, affect Sentiment Analysis (SA) tools. The study’s ultimate goal is to find out if FL hinders the performance (polarity detection) of SA systems due to the presence of ironic context. Our results indicate that computational intelligence approaches are more suitable in presence of irony and sarcasm in Twitter classification.

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Notes

  1. 1.

    https://github.com/jaredks/tweetokenize.

References

  1. Internet World Stats. http://www.internetworldstats.com (2015). Accessed October 2015

  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM (1993)

    Google Scholar 

  3. Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern information retrieval, vol. 463. ACM press, New York (1999)

    Google Scholar 

  4. Bamman, D., Smith, N.A.: Contextualized sarcasm detection on twitter. In: Ninth International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  5. Bikel, D., Zitouni, I.: Multilingual Natural Language Processing Applications: From Theory to Practice. IBM Press (2012)

    Google Scholar 

  6. Bogdanova, D., dos Santos, C., Barbosa, L., Zadrozny, B.: Detecting semantically equivalent questions in online user forums. CoNLL 2015, 123 (2015)

    Google Scholar 

  7. Bowes, A., Katz, A.: When sarcasm stings. Discourse Process. 48(4), 215–236 (2011)

    Article  Google Scholar 

  8. Bradley, M.M., Lang, P.J.: Affective norms for english words (anew): instruction manual and affective ratings. Technical report, Technical Report C-1, The Center for Research in Psychophysiology, University of Florida (1999)

    Google Scholar 

  9. Bughin, J., Corb, L., Manyika, J., Nottebohm, O., Chui, M., de Muller Barbat, B., Said, R.: The impact of internet technologies: search. Technical report, McKinsey & Company, High Tech Practice (2011)

    Google Scholar 

  10. Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., Piperidis, S. (eds.): Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014), European Language Resources Association (ELRA), Reykjavik, Iceland, 26–31 May 2014. http://www.lrec-conf.org/lrec2014

  11. Cambria, E., Livingstone, A., Hussain, A.: The hourglass of emotions. In: Cognitive Behavioural Systems, pp. 144–157. Springer (2012)

    Google Scholar 

  12. Cambria, E., Speer, R., Havasi, C., Hussain, A.: Senticnet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, vol. 10, p. 02 (2010)

    Google Scholar 

  13. 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 (2010)

    Google Scholar 

  14. De Smedt, T., Daelemans, W.: Pattern for python. J. Mach. Learn. Res. 13(1), 2063–2067 (2012)

    MATH  Google Scholar 

  15. Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2010)

    Article  Google Scholar 

  16. Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, vol. 6, pp. 417–422. Citeseer (2006)

    Google Scholar 

  17. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  18. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    Article  Google Scholar 

  19. Fellbaum, C.: Wordnet. In: Theory and Applications of Ontology: Computer applications (2010)

    Google Scholar 

  20. Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press (2012)

    Google Scholar 

  21. Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J. (eds.): Semeval-2015 task 11: sentiment analysis of figurative language in twitter. In: International Workshop on Semantic Evaluation (SemEval-2015). Denver, Colorado (2015)

    Google Scholar 

  22. GIménez, M., Pla, F., Hurtado, L.: Elirf: a svm approach for sa tasks in twitter at semeval-2015. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 574–581. Association for Computational Linguistics, Denver, Colorado (2015)

    Google Scholar 

  23. Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 27–38. ACM (2013)

    Google Scholar 

  24. González-Ibánez, 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, vol. 2, pp. 581–586. Association for Computational Linguistics (2011)

    Google Scholar 

  25. Grossman, D.A., Frieder, O.: Information Retrieval: Algorithms and Heuristics, vol. 15. Springer Science & Business Media (2012)

    Google Scholar 

  26. Gu, B., Ye, Q.: First step in social media: measuring the influence of online management responses on customer satisfaction. Prod. Oper. Manage. 23(4), 570–582 (2014)

    Article  Google Scholar 

  27. Hayta, A.B.: A study on the of effects of social media on young consumers’ buying behaviors. Management 65, 74 (2013)

    Google Scholar 

  28. Hearst, M.A.: Trends & controversies: support vector machines. IEEE Intell. Syst. 13(4), 18–28 (1998). http://dx.doi.org/10.1109/5254.708428

  29. Heerschop, B., Goossen, F., Hogenboom, A., Frasincar, F., Kaymak, U., de Jong, F.: Polarity analysis of texts using discourse structure. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1061–1070. ACM (2011)

    Google Scholar 

  30. Hosmer, D.W. Jr., Lemeshow, S.: Applied Logistic Regression. Wiley (2004)

    Google Scholar 

  31. Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 607–618. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  32. Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth ACM International Conference on Web search and Data Mining, pp. 537–546. ACM (2013)

    Google Scholar 

  33. Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  34. Ingle, A., Maheshwari, N., Sutrave, N., Akumarthi, S., Bhitre, T.: Sentiment analysis: Sarcasm detection of tweets. B.Sc, Disssertation, May 2014

    Google Scholar 

  35. Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn. Prentice-Hall Inc. (2008)

    Google Scholar 

  36. Kang, H., Yoo, S.J., Han, D.: Senti-lexicon and improved naïve bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst. Appl. 39(5), 6000–6010 (2012)

    Article  Google Scholar 

  37. Kende, M.: How the internet continues to sustain growth and innovation. Technical report 20, Analysys Mason Limited and The Internet Society (ISOC) (2012)

    Google Scholar 

  38. Kunneman, F., Liebrecht, C., van Mulken, M., van den Bosch, A.: Signaling sarcasm: from hyperbole to hashtag. Inf. Process. Manage. (2014)

    Google Scholar 

  39. Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31th International Conference on Machine Learning, ICML 2014, pp. 1188–1196, Beijing, China, 21–26 June 2014. http://jmlr.org/proceedings/papers/v32/le14.html

  40. Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, vol. 2, pp. 627–666 (2010)

    Google Scholar 

  41. Maynard, D., Greenwood, M.A.: Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In: Proceedings of 9th Language Resources and Evaluation Conference (2014)

    Google Scholar 

  42. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781

  43. Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation (2013). arXiv preprint arXiv:1309.4168

  44. Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, pp. 746–751. Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA, 9–14 June 2013. http://aclweb.org/anthology/N/N13/N13-1090.pdf

  45. Munková, D., Munk, M., Vozár, M.: Data pre-processing evaluation for text mining: transaction/sequence model. Procedia Comput. Sci. 18, 1198–1207 (2013)

    Article  Google Scholar 

  46. Ozdemir, C., Bergler, S.: Clac-sentipipe: semeval 2015 subtasks 10 b, e, and task 11. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 479–485. Association for Computational Linguistics, Denver, Colorado (2015)

    Google Scholar 

  47. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: International Conference on Language Resources and Evaluation, vol. 10, pp. 1320–1326 (2010)

    Google Scholar 

  48. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  49. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  50. 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., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  51. Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014), vol. 12, pp. 1532–1543 (2014)

    Google Scholar 

  52. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)

    Google Scholar 

  53. Ptáček, T., Habernal, I., Hong, J.: Sarcasm detection on czech and english twitter. In: Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers, COLING 2014, pp. 213–223. Dublin City University and Association for Computational Linguistics, Dublin, Ireland, August 2014. http://www.aclweb.org/anthology/C14-1022

  54. 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 (2015)

    Google Scholar 

  55. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, Malta, May 2010. http://is.muni.cz/publication/884893/en

  56. Reyes, A., Rosso, P.: On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowl. Inf. Syst. 40(3), 595–614 (2014)

    Article  Google Scholar 

  57. Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)

    Article  Google Scholar 

  58. Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)

    Article  Google Scholar 

  59. 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 the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, pp. 704–714. Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL, 18–21 October 2013. http://aclweb.org/anthology/D/D13/D13-1066.pdf

  60. Santos, C.D., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp. 1818–1826 (2014)

    Google Scholar 

  61. Sikos, L., Brown, S.W., Kim, A.E., Michaelis, L.A., Palmer, M.: Figurative language: “meaning” is often more than just a sum of the parts. In: AAAI Fall Symposium: Biologically Inspired Cognitive Architectures, pp. 180–185 (2008)

    Google Scholar 

  62. Silva, C., Ribeiro, B.: The importance of stop word removal on recall values in text categorization. In: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 3, pp. 1661–1666. IEEE (2003)

    Google Scholar 

  63. Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405. ACM (2011)

    Google Scholar 

  64. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 1555–1565. Association for Computational Linguistics, Baltimore, Maryland, June 2014. http://www.aclweb.org/anthology/P14-1146

  65. Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: liwc and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)

    Article  Google Scholar 

  66. Thelwall, M.: Heart and soul: sentiment strength detection in the social web with sentistrength. In: Proceedings of the CyberEmotions, pp. 1–14 (2013)

    Google Scholar 

  67. Van Rijsbergen, C.J.: A non-classical logic for information retrieval. Comput. J. 29(6), 481–485 (1986)

    Article  MATH  Google Scholar 

  68. Vanin, A.A., Freitas, L.A., Vieira, R., Bochernitsan, M.: Some clues on irony detection in tweets. In: Proceedings of the 22nd International Conference on World Wide Web companion, pp. 635–636. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  69. Walker, M.A., Anand, P., Abbott, R., Tree, J.E.F., Martell, C., King, J.: That is your evidence?: classifying stance in online political debate. Decis. Support Syst. 53(4), 719–729 (2012)

    Article  Google Scholar 

  70. Wallace, B.C., Do Kook Choe, L.K., Charniak, E.: Humans require context to infer ironic intent (so computers probably do, too). In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pp. 512–516 (2014)

    Google Scholar 

  71. Weichselbraun, A., Gindl, S., Scharl, A.: Extracting and grounding context-aware sentiment lexicons. IEEE Intell. Syst. 28(2), 39–46 (2013)

    Article  Google Scholar 

  72. Weiss, S.M., Indurkhya, N., Zhang, T., Damerau, F.: Text mining: predictive methods for analyzing unstructured information. Springer Science & Business Media (2010)

    Google Scholar 

  73. Weitzel, L., Freire, R.A., Quaresma, P., Gonçalves, T., Prati, R.C.: How does irony affect sentiment analysis tools? In: Progress in Artificial Intelligence—Proceedings of the 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, pp. 803–808. Coimbra, Portugal, 8–11 Sept 2015

    Google Scholar 

  74. Weitzel, L., de Oliveira, J.P.M., Quaresma, P.: Measuring the reputation in user-generated-content systems based on health information. Procedia Comput. Sci. 29, 364–378 (2014)

    Article  Google Scholar 

  75. Weitzel, L., de Oliveira, J.P.M., Quaresma, P.: Exploring trust to rank reputation in microblogging. In: Database and Expert Systems Applications. Lecture Notes in Computer Science, vol. 8056, pp. 434–441. Springer (2013)

    Google Scholar 

  76. Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W.: Multi-aspect sentiment analysis for chinese online social reviews based on topic modeling and hownet lexicon. Knowl.-Based Syst. 37, 186–195 (2013)

    Article  Google Scholar 

  77. Xu, H., Santus, E., Laszlo, A., Huang, C.: Llt-polyu: identifying sentiment intensity in ironic tweets. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 673–678. Association for Computational Linguistics, Denver, Colorado (2015)

    Google Scholar 

  78. Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M.: Fine-grained sentiment analysis with structural features. In: IJCNLP, pp. 336–344 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Departamento de Computação Rio das Ostras Rj, Univerisidade Federal Fluminense (UFF), Rio de Janeiro, Brazil

    Leila Weitzel

  2. Centro de Matemática, Computação e Cognição, Universidade Federal do ABC (UFABC), Santo André, SP, Brazil

    Ronaldo Cristiano Prati & Raul Freire Aguiar

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    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

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Weitzel, L., Prati, R.C., Aguiar, R.F. (2016). The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_3

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