Word Polarity Detection Using a Multilingual Approach

  • Cüneyd Murad Özsert
  • Arzucan Özgür
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)


Determining polarity of words is an important task in sentiment analysis with applications in several areas such as text categorization and review analysis. In this paper, we propose a multilingual approach for word polarity detection. We construct a word relatedness graph by using the relations in WordNet of a given language. We extend the graph by connecting the WordNets of different languages with the help of the Inter-Lingual-Index based on English WordNet. We develop a semi-automated procedure to produce a set of positive and negative seed words for foreign languages by using a set of English seed words. To identify the polarity of unlabeled words, we propose a method based on random walk model with commute time metric as proximity measure. We evaluate our multilingual approach for English and Turkish and show that it leads to improvement in performance for both languages.


Semantic orientation word polarity sentiment analysis random walk model commute time hitting time WordNet 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining Product Reputations on the Web. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 341–349 (2002)Google Scholar
  2. 2.
    Turney, P.D.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  3. 3.
    Popescu, A., Etzioni, O.: Extracting Product Features and Opinions from Reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing Association for Computational Linguistics, pp. 339–346 (2005)Google Scholar
  4. 4.
    Hassan, A., Qazvinian, V., Radev, D.: What’s with the Attitude? Identifying Sentences with Attitude in Online Discussions. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1245–1255 (2010)Google Scholar
  5. 5.
    Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  6. 6.
    Stone, P., Dunphy, D., Smith, M., Ogilvie, D.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1966)Google Scholar
  7. 7.
    Takamura, H., Inui, T., Okumura, M.: Extracting Semantic Orientations of Words Using Spin Model. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 133–140 (2005)Google Scholar
  8. 8.
    Hassan, A., Radev, D.: Identifying Text Polarity Using Random Walks. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 395–403 (2010)Google Scholar
  9. 9.
    Vossen, P.: Eurowordnet: A Multilingual Database with Lexical Semantic Networks. Kluwer Academic Publishers, Norwell (1998)zbMATHGoogle Scholar
  10. 10.
    Tufiş, D., Cristea, D., Stamou, S.: Balkanet: Aims, Methods, Results and Perspectives. A General Overview. Romanian Journal on Science and Technology of Information 7, 9–43 (2004)Google Scholar
  11. 11.
    Bilgin, O., Çetinoglu, Ö., Oflazer, K.: Building a Wordnet for Turkish. Romanian Journal on Information Science and Technology 7, 163–172 (2004)Google Scholar
  12. 12.
    Turney, P.D., Littman, M.L.: Measuring Praise and Criticism: Inference of Semantic Orientation from Association. ACM Transactions on Information Systems 21(4), 315–346 (2003)CrossRefGoogle Scholar
  13. 13.
    Hassan, A., Abu-Jbara, A., Jha, R., Radev, D.: Identifying the Semantic Orientation of Foreign Words. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 592–597 (2011)Google Scholar
  14. 14.
    Lovasz, L.: Random Walks on Graphs: A Survey. Bolyai Society Mathematical Studies 2, 353–398 (1996)MathSciNetGoogle Scholar
  15. 15.
    Sarkar, P.: Tractable Algorithms for Proximity Search on Large Graphs. Ph.D. Thesis, Carnegie Mellon University (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cüneyd Murad Özsert
    • 1
  • Arzucan Özgür
    • 1
  1. 1.Department of Computer EngineeringBoğaziçi UniversityBebekTurkey

Personalised recommendations