Advertisement

Graph-Based Semi-supervised Learning for Cross-Lingual Sentiment Classification

  • Mohammad Sadegh HajmohammadiEmail author
  • Roliana Ibrahim
  • Ali Selamat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9011)

Abstract

Cross-lingual sentiment classification aims to use labelled sentiment data in one language for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly transfer information from one language to another. However, different term distribution between translated data and original data can lead to low performance in cross-lingual sentiment classification. Further, due to the existence of differing structures and writing styles between different languages, using only information of labelled data from a different language cannot show a good performance in this classification task. To overcome these problems, we propose a new model which uses sentiment information of unlabelled data as well as labelled data in a graph-based semi-supervised learning approach so as to incorporate intrinsic structure of unlabelled data from the target language into the learning process. The proposed model was applied to book review datasets in two different languages. Experiments have shown that our model can effectively improve the cross-lingual sentiment classification performance in comparison with some baseline methods.

Keywords

Cross-lingual Sentiment classification Graph-based Semi-supervised learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Liu, B., Zhang, L.: A Survey of Opinion Mining and Sentiment Analysis. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer US(2012)Google Scholar
  2. Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs (2006)Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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, Philadelphia, Pennsylvania: Association for Computational Linguistics (2002)Google Scholar
  5. Taboada, M., et al.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  6. 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, Association for Computational Linguistics (2002)Google Scholar
  7. Moraes, R., Valiati, J.F., Neto, W.P.G.: Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Syst. Appl. 40(2), 621–633 (2013)CrossRefGoogle Scholar
  8. Montoyo, A., Martínez-Barco, P., Balahur, A.: Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments. Decis. Support Syst. 53(4), 675–679 (2012)CrossRefGoogle Scholar
  9. Wan, X.: Bilingual co-training for sentiment classification of Chinese product reviews. Comput. Linguist. 37(3), 587–616 (2011)CrossRefGoogle Scholar
  10. Hajmohammadi, M.S., Ibrahim, R., Selamat, A.: Bi-view semi-supervised active learning for cross-lingual sentiment classification. Inf. Process. Manage. 50(5), 718–732 (2014a)CrossRefGoogle Scholar
  11. Hajmohammadi, M.S., Ibrahim, R., Selamat, A.: Cross-lingual sentiment classification using multiple source languages in multi-view semi-supervised learning. Eng. Appl. Artif. Intell. 36, 195–203 (2014b)CrossRefGoogle Scholar
  12. Balahur, A., Turchi, M.: Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language (2013)Google Scholar
  13. Prettenhofer, P., Stein, B.: Cross-language text classification using structural correspondence learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1118–1127. Association for Computational Linguistics, Uppsala, Sweden (2010)Google Scholar
  14. Perea-Ortega, J.M., et al.: Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches. J. Am. Soc. Inform. Sci. Technol. 64(9), 1759–1962 (2013)CrossRefGoogle Scholar
  15. Banea, C., Mihalcea, R., Wiebe, J.: Multilingual subjectivity: are more languages better? In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 28–36. Association for Computational Linguistics: Beijing, China (2010)Google Scholar
  16. Balahur, A., Turchi, M.: Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Comput. Speech Lang. 28(1), 56–75 (2014)CrossRefGoogle Scholar
  17. Hajmohammadi, M.S., Ibrahim, R., Selamat, A.: Density based active self-training for cross-lingual sentiment classification. In: Jeong, H.Y., Obaidat, M.S., Yen, N.Y., Park, J.J. (eds.) Advanced in Computer Science and Its Applications. LNEE, vol. 279, pp. 1053–1059. Springer, Heidelberg (2014c)CrossRefGoogle Scholar
  18. Pan, J., Xue, G.-R., Yu, Y., Wang, Y.: Cross-lingual sentiment classification via bi-view non-negative matrix tri-factorization. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part I. LNCS(LNAI), vol. 6634, pp. 289–300. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. Mihalcea, R., Banea, C., Wiebe, J.: Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (2007)Google Scholar
  20. Wan, X.: Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 553–561. Association for Computational Linguistics, Honolulu (2008)Google Scholar
  21. Prettenhofer, P., Stein, B.: Cross-Lingual Adaptation Using Structural Correspondence Learning. ACM Trans. Intell. Syst. Technol. 3(1), 1–22 (2011)CrossRefGoogle Scholar
  22. Wan, X.: Co-training for cross-lingual sentiment classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 235–243. Association for Computational Linguistics: Suntec, Singapore (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohammad Sadegh Hajmohammadi
    • 1
    Email author
  • Roliana Ibrahim
    • 2
  • Ali Selamat
    • 2
  1. 1.Department of Computer Engineering, Sirjan BranchIslamic Azad UniversitySirjanIran
  2. 2.Software Engineering Research Group, Faculty of ComputingUniversiti Teknologi MalaysiaJohorMalaysia

Personalised recommendations