A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs

  • Michelle Annett
  • Grzegorz Kondrak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)


With the ever-growing popularity of online media such as blogs and social networking sites, the Internet is a valuable source of information for product and service reviews. Attempting to classify a subset of these documents using polarity metrics can be a daunting task. After a survey of previous research on sentiment polarity, we propose a novel approach based on Support Vector Machines. We compare our method to previously proposed lexical-based and machine learning (ML) approaches by applying it to a publicly available set of movie reviews. Our algorithm will be integrated within a blog visualization tool.


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  1. 1.
    Tirapat, T., Espiritu, C., Stroulia, E.: Taking the Community’s Pulse, One Blog at a Time. In: Proceedings of the Sixth International Conference on Web Engineering, Palo Alto, CA, July 2006, pp. 169–176 (2006)Google Scholar
  2. 2.
    Kennedy, A., Inkpen, D.: Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters. Computational Intelligence, 110–125 (2006)Google Scholar
  3. 3.
    Turney, P.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of ACL, Philadelphia, PA, July 2002, pp. 417–424 (2002)Google Scholar
  4. 4.
    Kamps, J., Marx, M., Mokken, R.J.: Using WordNet to Measure Semantic Orientation of Adjectives. In: LREC 2004, vol. IV, pp. 1115–1118 (2004)Google Scholar
  5. 5.
    Hatzivassiloglou, V., Wiebe, J.: Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In: Proceedings of the 18th International Conference on Computational Linguistics, New Brunswick, NJ (2000)Google Scholar
  6. 6.
    Andreevskaia, A., Bergler, S., Urseanu, M.: All Blogs Are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs. In: International Conference on Weblogs and Social Media (ICWSM-2007), Boulder, CO (2007)Google Scholar
  7. 7.
    Turney, P.D., Littman, M.L.: Measuring Praise and Criticism: Inference of Semantic Orientation from Association. ACM Transactions on Information Systems, 315–346 (2003)Google Scholar
  8. 8.
    Akshay, J.: A Framework for Modeling Influence, Opinions and Structure in Social Media. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, Vancouver, BC, July 2007, pp. 1933–1934 (2007)Google Scholar
  9. 9.
    Durant, K., Smith, M.: Mining Sentiment Classification from Political Web Logs. In: Proceedings of Workshop on Web Mining and Web Usage Analysis of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (WebKDD-2006), Philadelphia, PA (August 2006)Google Scholar
  10. 10.
    Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)Google Scholar
  11. 11.
    Yahoo! Search Web Services (October 2007), Online http://developer.yahoo.com/search/
  12. 12.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up? Sentiment Classification Using Machine Learning Techniques. In: Proceedings of EMNLP-02, Association for Computational Linguistics, Philadelphia, PA, pp. 79–86 (2002)Google Scholar
  13. 13.
  14. 14.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. Language, Speech, and Communication Series. MIT Press, Cambridge (1998)MATHGoogle Scholar
  15. 15.
    Toutanova, K., Manning, C.D.: Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In: Proceedings of EMNLP/VLC-2000, Hong Kong, China, pp. 63–71 (2000)Google Scholar
  16. 16.
    Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  17. 17.
    Joachims, T.: Making Large-Scale SVM Learning Practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 169–184. MIT-Press, Cambridge (1999)Google Scholar
  18. 18.
    Sato, N., Anse, M., Tabe, T.: A Method for Constructing a Movie-Selection Support System Based on Kansei Engineering. In: Smith, M.J., Salvendy, G. (eds.) HCII 2007. LNCS, vol. 4557, pp. 526–534. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michelle Annett
    • 1
  • Grzegorz Kondrak
    • 1
  1. 1.Department of Computing ScienceUniversity of Alberta 

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