Advertisement

Sentence-Level Sentiment Polarity Classification Using a Linguistic Approach

  • Luke Kien-Weng Tan
  • Jin-Cheon Na
  • Yin-Leng Theng
  • Kuiyu Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7008)

Abstract

Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall F1-score, and exceeding 10% in terms of positive F1-score when compared to a Typed-dependency only approach.

Keywords

Sentiment analysis polarity classification linguistic approach 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cohen, J.A.: Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)CrossRefGoogle Scholar
  2. 2.
    Hassan, A., Qazvinian, V., Radev, D.R.: What’s with the attitude? A study of participant attitude in multi-party online discussions. In: Empirical Methods on Natural Language Processing, pp. 1245–1255. ACL (2010)Google Scholar
  3. 3.
    Jakob, N., Weber, S.H., Muller, M.C., Gurevych, I.: Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: Conference on Information and Knowledge Management, pp. 57–64. ACM, New York (2009)Google Scholar
  4. 4.
    Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2), 110–125 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Moilanen, K., Pulman, S.: Sentiment Composition. In: Recent Advances in Natural Language Processing, pp. 378–382 (2007)Google Scholar
  6. 6.
    Polanyi, L., Zaenen, A.: Contextual Valence Shifters. Computing Attitude and Affect in Text: Theory and Applications 20, 1–10 (2006)CrossRefGoogle Scholar
  7. 7.
    Quirk, R., Greenbaum, S., Leech, G., Svartvik, J.: A Comprehensive Grammar of the English Language. Longman, Redwood City (1985)Google Scholar
  8. 8.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Empirical Methods on Natural Language Processing, pp. 105–112. ACL (2003)Google Scholar
  9. 9.
    Shaikh, M.A.M., Prendinger, H., Ishizuka, M.: Sentiment Assessment of Text By Analyzing Linguistic Features And Contextual Valence Assignment. Applied Artificial Intelligence 22(6), 558–601 (2008)CrossRefGoogle Scholar
  10. 10.
    Thet, T.T., Na, J.-C., Khoo, C.S.G.: Aspect-Based Sentiment Analysis of Movie Reviews on Discussion Boards. Journal of Information Science 36(6), 823–848 (2010)CrossRefGoogle Scholar
  11. 11.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistic 35(3), 399–433 (2009)CrossRefGoogle Scholar
  12. 12.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. ACL (2005)Google Scholar
  13. 13.
    Wilson, T., Wiebe, J., Hwa, R.: Recognizing Strong and Weak Opinion Clauses. Computational Intelligence 22(2), 73–99 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zafarani, R., Liu, H.: Social Computing Data Repository at ASU Arizona State University, School of Computing, Informatics and Decision Systems Engineering (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luke Kien-Weng Tan
    • 1
  • Jin-Cheon Na
    • 1
  • Yin-Leng Theng
    • 1
  • Kuiyu Chang
    • 2
  1. 1.Wee Kim Wee School of Communication and InformationNanyang Technological UniversitySingapore
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

Personalised recommendations