Skip to main content

Opinion Classification Techniques Applied to a Spanish Corpus

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6716))

Abstract

Sentiment analysis is a new challenging task related to Text Mining and Natural Language Processing. Although there are some current works, most of them only focus on English texts. Web pages, information and opinions on the Internet are increasing every day, and English is not the only language used to write them. Other languages like Spanish are increasingly present so we have carried out some experiments over a Spanish film reviews corpus. In this paper we present several experiments using five classification algorithms (SVM, Nave Bayes, BBR, KNN, C4.5). The results obtained are very promising and encourage us to continue investigating in this line.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahmad, K., Cheng, D., Almas, Y.: Multi-lingual Sentiment Analysis of Financial News Streams. In: Proceedings of Science, GRID 2006 (2006)

    Google Scholar 

  2. Boldrini, E., Balahur, A., Martínez-Barco, P., Montoyo, A.: EmotiBlog: an annotation scheme for emotion detection and analysis in non-traditional textual genres. In: DMIN, pp. 491–497. CSREA Press

    Google Scholar 

  3. Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. In: Computational Linguistics, vol. 22(2). MIT Press, Cambridge (1996)

    Google Scholar 

  4. Chang, C.C., Lin, C.J.: LIBSVM: a Library for Support Vector Machines (2001)

    Google Scholar 

  5. Cruz, F.L., Troyano, J.A., Enriquez, F., Ortega, J.: Clasificación de documentos basada en la opinión: experimentos con un corpus de críticas de cine en español. Procesamiento de Lenguaje Natural 41 (2008)

    Google Scholar 

  6. Denecke, K.: Using SentiWordNet for multilingual sentiment analysis. In: ICDE Workshops, pp. 507–512. IEEE Computer Society, Los Alamitos (2008)

    Google Scholar 

  7. Genkin, A., Lewis, D., Madigan, D.: Large-Scale Bayesian Logistic Regression for Text Categorization (2004)

    Google Scholar 

  8. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  9. Ortiz-Martos, A., Martín-Valdivia, M.T., Ureña-Lopez, L.A., Cumbreras-García, M.A.: Detección automática de Spam utilizando Regresión Logística Bayesiana. Procesamiento del Lenguaje Natural 35, 127–133 (2005)

    Google Scholar 

  10. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundation and Trends in Information Retrieval 2(1-2), 1–135 (2008)

    Google Scholar 

  11. Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)

    Google Scholar 

  12. Quinlan, J.R.: Programs for Machine Learning. Morgan Kaurfman, San Francisco (1993)

    Google Scholar 

  13. Sebastiani, F.: Machine Learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1), 1–47 (2002)

    Article  Google Scholar 

  14. Esuli, A., Sebastiani, F.: SentiWordNet: A publicly Available Lexical Resource for Opinion Mining. In: Proceedings of Language Resources and Evaluation, LREC (2006)

    Google Scholar 

  15. Stone, P.J.: The General Inquierer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1996)

    Google Scholar 

  16. Tan, S., Zhang, J.: An empirical study of sentiment analysis for Chinese documents. Expert System with Applications 34, 2622–2629 (2008)

    Article  Google Scholar 

  17. Vapnik, V.: The Nature of Statistical Learning Theory. Springer-Verlag, New York (1995)

    Book  MATH  Google Scholar 

  18. Zhang, C., Zeng, D., Li, J., Wang, F.-Y., Zuo, W.: Sentiment analysis of Chinese documents: From sentence to document level. JASIST 60, 2474–2487 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña-López, L.A. (2011). Opinion Classification Techniques Applied to a Spanish Corpus. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22327-3_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22326-6

  • Online ISBN: 978-3-642-22327-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics