Advertisement

Language-Independent Sentiment Analysis with Surrounding Context Extension

  • Tomáš KinclEmail author
  • Michal Novák
  • Jiří Přibil
  • Pavel Štrach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9182)

Abstract

Expressing attitudes and opinions towards various entities (i.e. products, companies, people and events) has become pervasive with the recent proliferation of social media. Monitoring of what customers think is a key task for marketing research and opinion surveys, while measuring customers’ preferences or media monitoring have become a fundamental part of corporate activities. Most experiments on automated sentiment analysis focus on major languages (English, but also Chinese); minor or morphologically rich languages are addressed rather sparsely. Moreover, to improve the performance of machine-learning based classifiers, the models are often complemented with language-dependent components (i.e. sentiment lexicons). Such combined approaches provide a high level of accuracy but are limited to a single language or a single thematic domain.

This paper aims to contribute to this field and introduces an experiment utilizing a language– and domain– independent model for sentiment analysis. The model has been previously tested on multiple corpora, providing a trade-off between generality and the classification performance of the model. In this paper, we suggest a further extension of the model utilizing the surrounding context of the classified documents.

Keywords

Sentiment analysis Cross-domain Cross-language Document surrounding context 

References

  1. 1.
  2. 2.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)CrossRefGoogle Scholar
  3. 3.
    Balahur, A.: Sentiment analysis in social media texts. In: WASSA 2013, p. 120 (2013)Google Scholar
  4. 4.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56, 82–89 (2013)CrossRefGoogle Scholar
  5. 5.
    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, vol. 10, pp. 79–86 (2002)Google Scholar
  6. 6.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the International Conference on Language Resources and Evaluation, LREC, pp. 1320-1326 (2010)Google Scholar
  7. 7.
    Tsarfaty, R., Seddah, D., Goldberg, Y., Kuebler, S., Candito, M., Foster, J., Versley, Y., Rehbein, I., Tounsi, L.: Statistical parsing of morphologically rich languages (SPMRL): what, how and whither. In: Proceedings of the First Workshop on Statistical Parsing of Morphologically-Rich Languages, NAACL HLT 2010, pp. 1–12. Association for Computational Linguistics (2010)Google Scholar
  8. 8.
    Kincl, T., Novák, M., Přibil, J.: Getting inside the minds of the customers: automated sentiment analysis. In: European Conference on Management Leadership and Governance ECMLG 2013, pp. 122–129. Alpen-Adria Universität Klagenfurt, Austria (2013)Google Scholar
  9. 9.
    Chmiel, A., Sienkiewicz, J., Thelwall, M., Paltoglou, G., Buckley, K., Kappas, A., Hołyst, J.A.: Collective emotions online and their influence on community life. PLoS ONE 6, e22207 (2011)CrossRefGoogle Scholar
  10. 10.
    Brychcín, T., Habernal, I.: Unsupervised improving of sentiment analysis using global target context. In: International Conference Recent Advances in Natural Language Processing (RANLP 2013) (2013)Google Scholar
  11. 11.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intel. Syst. Technol. (TIST) 2, 1–39 (2011)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150. Association for Computational Linguistics (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tomáš Kincl
    • 1
    Email author
  • Michal Novák
    • 1
  • Jiří Přibil
    • 1
  • Pavel Štrach
    • 2
  1. 1.University of EconomicsPragueCzech Republic
  2. 2.Upper Austria University of Applied SciencesSteyrAustria

Personalised recommendations