Advertisement

String Kernels for Polarity Classification: A Study Across Different Languages

  • Rosa M. Giménez-Pérez
  • Marc Franco-Salvador
  • Paolo Rosso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10859)

Abstract

The polarity classification task has as objective to automatically deciding whether a subjective text is positive or negative. Using a cross-domain setting implies the use of different domains for the training and testing. Recently, string kernels, a method which does not employ domain adaptation techniques has been proposed. In this work, we analyse the performance of this method across four different languages: English, German, French and Japanese. Experimental results show the strong potential of this approach independently from the language.

Keywords

String kernels Sentiment analysis Single-domain Cross-domain 

Notes

Acknowledgements

The work of the third author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).

References

  1. 1.
    Baudat, G., Anouar, F.: Generalized discriminant analysis using a Kernel approach. Neural Comput. 12(10), 2385–2404 (2000)CrossRefGoogle Scholar
  2. 2.
    Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the Annual Meeting of the Association of Computational Linguistics, pp. 440–447 (2007)Google Scholar
  3. 3.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5 (2017)Google Scholar
  4. 4.
    Giménez-Pérez, R.M., Franco-Salvador, M., Rosso, P.: Single and cross-domain polarity classification using string kernels. In: Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, p. 558 (2017)Google Scholar
  5. 5.
    Ionescu, R.T., Popescu, M., Cahill, A.: Can characters reveal your native language? A language-independent approach to native language identification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1363–1373 (2014)Google Scholar
  6. 6.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86 (2002)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rosa M. Giménez-Pérez
    • 1
  • Marc Franco-Salvador
    • 2
  • Paolo Rosso
    • 1
  1. 1.Universitat Politècnica de ValènciaValenciaSpain
  2. 2.Symanto ResearchNurembergGermany

Personalised recommendations