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Application of Rough Set Theory to Sentiment Analysis of Microblog Data

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Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 43))

Abstract

Microblogging has become a popular medium for broadcasting short text messages of 140 characters or less through social networks. There are millions of such posts shared each day, publically expressing sentiment over a variety of topics using popular Internet sites such as Twitter.com, Plurk.com, and identi.ca. Most of the existing works have focused on polarity sentiment analysis of these data by applying machine learning algorithms. In this chapter, we show that the rough set theory introduced by Pawlak provides an effective tool for deriving new perspectives of sentiment analysis from microblogging messages. More specifically, we introduce the use of rough set theory to formulate sentimental approximation spaces based on key words for assessing sentiment of microblogging messages. The sentimental approximation space provides contextual sentiment from the entire collection of messages, and it enables the evaluation of sentiment of different subjects, not in isolation, but in context. Sentiment, itself, is subjective. The degree of emotion that a word invokes in one person will be different than in another. It is for this reason that sentimental approximation space offers potentially more insightful information about a subject than simple polarity answers of positive or negative.

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References

  1. Twitter hits 50 million tweets per day, http://mashable.com/2010/02/22/twitter-50-million-tweets/

  2. Sagolla, D.: 140 Characters: A Style Guide for the Short Form. Wiley (2009) ISBN 0470556137, 978-0470556139

    Google Scholar 

  3. Kim, D., Jo, Y., Moon, I.-C., Oh, A.: Analysis of Twitter Lists as a Potential Source for Discovering Latent Characteristics of Users. In: Workshop on Microblogging at the ACM Conference on Human Factors in Computer Systems (CHI 2010) (2010)

    Google Scholar 

  4. Cheong, M., Lee, V.: A study on detecting patterns in twitter intra-topic user and message clustering. In: 2010 International Conference on Pattern Recognition, pp. 3125–3128 (2010)

    Google Scholar 

  5. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proc. of the Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (July 2002)

    Google Scholar 

  6. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. In: Proc. of the 4th International Conf. on Computer and Information Technology (CIT 2004), pp. 1147–1152 (2004)

    Google Scholar 

  7. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proc. of the Seventh Conf. on International Language Resources and Evaluation (LREC 2010) (May 2010)

    Google Scholar 

  8. Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proc. of ACL 2005, 43rd Meeting of the Association for Computational Linguistics, pp. 43–48 (2005)

    Google Scholar 

  9. Java, A., Song, X., Fin, T., Tseng, B.: Why We Twitter: Understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)

    Google Scholar 

  10. Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about Twitter. In: Proceedings of the 1st Workshop on Online Social Networks (WOSN 2008), pp. 19–24 (2008)

    Google Scholar 

  11. Maron, M.: Automatic indexing: An experimental inquiry. J. Assoc. Comput. Mach. 8(3), 404–417 (1961)

    Article  MATH  Google Scholar 

  12. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)

    Article  Google Scholar 

  13. Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Communication of ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  14. Pawlak, Z.: Rough sets: Basic notions. International Journal of Computer and Information Science 11(15), 344–356 (1982)

    MathSciNet  Google Scholar 

  15. Pawlak, Z.: Rough Sets and Decision Tables. In: Skowron, A. (ed.) SCT 1984. LNCS, vol. 208, pp. 186–196. Springer, Heidelberg (1985)

    Google Scholar 

  16. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing (1991)

    Google Scholar 

  17. Pawlak, Z., Grzymała-Busse, J., Słowiński, R., Ziarko, W.: Rough sets. Communication of ACM 38(11), 89–95 (1995)

    Article  Google Scholar 

  18. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177(1), 28–40 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  20. Ziarko, W.: Variable precision rough sets model. Journal of Computer and System Sciences 46(1), 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  21. Choudhury, M.D., Lin, Y.-R., Sundaram, H., Candan, K.S., Xie, L., Kelliher, A.: How does the sampling strategy impact the discovery of information diffusion in social media? In: Proc. of the 4th Int’l AAAI Conference on Weblogs and Social Media, George Washington University, Washington, DC, May 23-26 (2010)

    Google Scholar 

  22. Twitter compatible positive and negative word list, http://www.twitrratr.com

  23. Ślęzak, D., Ziarko, W.: The investigation of the Bayesian rough set model. International Journal of Approximate Reasoning 40, 81–91 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. Deerwester, S., et al.: Improving information retrieval with latent semantic indexing. In: Proceedings of the 51st Annual Meeting of the American Society for Information Science, vol. 25, pp. 36–40 (1988)

    Google Scholar 

  25. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, 391–407 (1990)

    Article  Google Scholar 

  26. Chan, C.-C.: Incremental learning of production rules from examples under uncertainty: A rough set approach. Int. J. of Software Engineering and Knowledge Engineering 1(4), 439–461 (1991)

    Article  Google Scholar 

  27. Chan, C.-C.: A rough set approach to attribute generalization in data mining. Information Sciences 107(1-4), 169–176 (1998)

    Article  MathSciNet  Google Scholar 

  28. Shan, N., Ziarko, W.: Data-based acquisition and incremental modification of classification rules. Computational Intelligence 11(2), 357–370 (1995)

    Article  MathSciNet  Google Scholar 

  29. Li, T., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowledge-Based Systems 20(5), 485–494 (2007)

    Article  Google Scholar 

  30. Kryszkiewicz, M.: Rough set approach to incomplete information systems. Information Sciences 112, 39–49 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  31. Grzymała-Busse, J.W.: Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 58–68. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Akron, Akron, OH, 44325-4003, USA

    Chien-Chung Chan & Kathy J. Liszka

Authors
  1. Chien-Chung Chan
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  2. Kathy J. Liszka
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Corresponding author

Correspondence to Chien-Chung Chan .

Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, Warsaw, 02097, Poland

    Andrzej Skowron

  2. Institute of Computer Science, University of Rzeszów, ul. Dekerta 2, Rzeszów, 35-030, Poland

    Zbigniew Suraj

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Chan, CC., Liszka, K.J. (2013). Application of Rough Set Theory to Sentiment Analysis of Microblog Data. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30341-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-30341-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30340-1

  • Online ISBN: 978-3-642-30341-8

  • eBook Packages: EngineeringEngineering (R0)

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