Cross-Lingual Projections vs. Corpora Extracted Subjectivity Lexicons for Less-Resourced Languages

  • Xabier Saralegi
  • Iñaki San Vicente
  • Irati Ugarteburu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)


Subjectivity tagging is a prior step for sentiment annotation. Both machine learning based approaches and linguistic knowledge based ones profit from using subjectivity lexicons. However, most of these kinds of resources are often available only for English or other major languages. This work analyses two strategies for building subjectivity lexicons in an automatic way: by projecting existing subjectivity lexicons from English to a new language, and building subjectivity lexicons from corpora. We evaluate which of the strategies performs best for the task of building a subjectivity lexicon for a less-resourced language (Basque). The lexicons are evaluated in an extrinsic manner by classifying subjective and objective text units belonging to various domains, at document- or sentence-level. A manual intrinsic evaluation is also provided which consists of evaluating the correctness of the words included in the created lexicons.


Sentiment Analysis Subjectivity Detection Less Resourced Languages 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, pp. 129–136 (2003)Google Scholar
  2. 2.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, ACL 2004 (2004)Google Scholar
  3. 3.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT/EMNLP 2005, pp. 347–354 (2005)Google Scholar
  4. 4.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112 (2003)Google Scholar
  5. 5.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2), 165–210 (2005)CrossRefGoogle Scholar
  6. 6.
    Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of LREC 2006, pp. 417–422 (2006)Google Scholar
  7. 7.
    Stone, P., Dunphy, D., Smith, M.: The general inquirer: A computer approach to content analysis (1966)Google Scholar
  8. 8.
    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, ACL 2002, Philadelphia, Pennsylvania, p. 417 (2002)Google Scholar
  9. 9.
    Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of HTML documents. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic, pp. 1075–1083 (2007)Google Scholar
  10. 10.
    Mihalcea, R., Banea, C., Wiebe, J.: Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, vol. 45, p. 976 (2007)Google Scholar
  11. 11.
    Banea, C., Mihalcea, R., Wiebe, J., Hassan, S.: Multilingual subjectivity analysis using machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, pp. 127–135 (2008)Google Scholar
  12. 12.
    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, EMNLP 2008, pp. 553–561 (2008)Google Scholar
  13. 13.
    Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: OpinionFinder. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, Vancouver, British Columbia, Canada, pp. 34–35 (2005)Google Scholar
  14. 14.
    Quirk, R., Greenbaum, S., Leech, G., Svartvik, J.: A comprehensive grammar of the English language. Pearson Education India (1985)Google Scholar
  15. 15.
    Wilson, T.A.: Fine-grained Subjectivity and Sentiment Analysis: Recognizing the Intensity, Polarity, and Attitudes of Private States. ProQuest (2008)Google Scholar
  16. 16.
    Wiebe, J.M., Bruce, R.F., O’Hara, T.P.: Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, ACL 1999, pp. 246–253 (1999)Google Scholar
  17. 17.
    Wang, X., Fu, G.H.: Chinese subjectivity detection using a sentiment density-based naive bayesian classifier. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 6, pp. 3299–3304 (2010)Google Scholar
  18. 18.
    Das, A., Bandyopadhyay, S.: Subjectivity detection in english and bengali: A CRF-based approach. In: Proceeding of ICON (2009)Google Scholar
  19. 19.
    Das, A., Bandyopadhyay, S.: Theme detection an exploration of opinion subjectivity. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, pp. 1–6 (September 2009)Google Scholar
  20. 20.
    Yu, N., Kübler, S.: Filling the gap: Semi-supervised learning for opinion detection across domains. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pp. 200–209 (2011)Google Scholar
  21. 21.
    Jijkoun, V., de Rijke, M.: Bootstrapping subjectivity detection. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, New York, NY, USA, pp. 1125–1126 (2011)Google Scholar
  22. 22.
    Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, Edmonton, Canada, pp. 25–32 (2003)Google Scholar
  23. 23.
    Baroni, M., Vegnaduzzo, S.: Identifying subjective adjectives through web-based mutual information. In: Proceedings of the 7th Konferenz zur Verarbeitung Natürlicher Sprache, KONVENS 2004, pp. 613–619 (2004)Google Scholar
  24. 24.
    Jijkoun, V., Hofmann, K.: Generating a non-english subjectivity lexicon: relations that matter. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009, pp. 398–405 (2009)Google Scholar
  25. 25.
    Banea, C., Mihalcea, R., Wiebe, J.: Multilingual sentiment and subjectivity analysis. In: Multilingual Natural Language Processing (2011)Google Scholar
  26. 26.
    Wiebe, J., Wilson, T., Bell, M.: Identifying collocations for recognizing opinions. In: Proceedings of the ACL 2001 Workshop on Collocation: Computational Extraction, Analysis and Exploitation, pp. 24–31 (2001)Google Scholar
  27. 27.
    Maks, I., Vossen, P.: Building a fine-grained subjectivity lexicon from a web corpus. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey (May 2012)Google Scholar
  28. 28.
    Wang, D., Liu, Y.: A cross-corpus study of unsupervised subjectivity identification based on calibrated em. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, WASSA 2011, pp. 161–167 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xabier Saralegi
    • 1
  • Iñaki San Vicente
    • 1
  • Irati Ugarteburu
    • 1
  1. 1.Elhuyar FoundationUsurbilSpain

Personalised recommendations