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LIWC-Based Sentiment Analysis in Spanish Product Reviews

  • Estanislao López-López
  • María del Pilar Salas-Zárate
  • Ángela Almela
  • Miguel Ángel Rodríguez-García
  • Rafael Valencia-García
  • Giner Alor-Hernández
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 290)

Abstract

Opinion mining is the study of opinions and emotions of authors about specific topics on the Web. Opinion mining identifies whether the opinion about a given topic, expressed in a document, is positive or negative. Nowadays, with the exponential growth of social medial i.e. blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision making about a product or service. This paper investigates technological products reviews mining using the psychological and linguistic features obtained through of text analysis software, LIWC. Furthermore, an analysis of the classification techniques J48, SMO, and BayesNet has been performed by using WEKA (Waikato Environment for Knowledge Analysis). This analysis aims to evaluate the classifying potential of the LIWC (Linguistic Inquiry and Word Count) dimensions on written opinions in Spanish. All in all, findings have revealed that the combination of the four LIWC dimensions provides better results than the other combinations and individual dimensions, and that SMO is the algorithm which has obtained the best results.

Keywords

sentiment analysis opinion mining LIWC machine learning 

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References

  1. 1.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2, 1–135 (2008)CrossRefGoogle Scholar
  2. 2.
    Bouckaert, R.R., Frank, E., Hall, M.A., Holmes, G., Pfahringer, B., Reutemann, P., Wit-ten, I.H.: WEKA–experiences with a java open–source project. Journal of Machine Learning Research 11, 2533–2541 (2010)zbMATHGoogle Scholar
  3. 3.
    Rushdi Saleh, M., Martín Valdivia, M., Montejo Ráez, A., Ureña López, L.: Experiments with SVM to classify opinions in different domains. Expert Systems with Applications 38, 14799–14804 (2011)CrossRefGoogle Scholar
  4. 4.
    Moraes, R., Valiati, J.F., Gavião Neto, W.P.: Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications 40, 621–633 (2013)CrossRefGoogle Scholar
  5. 5.
    Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences 181, 1138–1152 (2011)CrossRefGoogle Scholar
  6. 6.
    Chen, L., Liu, C., Chiu, H.: A neural network based approach for sentiment classification in the blogosphere. Journal of Informetrics 5, 313–322 (2011)CrossRefGoogle Scholar
  7. 7.
    He, Y., Zhou, D.: Self-training from labeled features for sentiment analysis. Information Processing and Management 47, 606–616 (2011)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Zhai, Z., Xu, H., Kang, B., Jia, P.: Exploiting effective features for chinese sentiment classification. Expert Systems with Applications 38, 9139–9146 (2011)CrossRefGoogle Scholar
  9. 9.
    Molina González, M.D., Martínez Cámara, E., Martín Valdivia, M.T., Perea Ortega, J.M.: Semantic orientation for polarity classification in Spanish reviews. Expert Systems with Applications 40, 7250–7257 (2013)CrossRefGoogle Scholar
  10. 10.
    Stiles, W.B.: Describing talk: A taxonomy of verbal response modes. Sage, Newbury Park (1992)Google Scholar
  11. 11.
    Pennebaker, J.W., Mayne, T., Francis, M.E.: Linguistic predictors of adaptive bereavement. Journal of Personality and Social Psychology 72, 863–871 (1997)CrossRefGoogle Scholar
  12. 12.
    Francis, M.E., Pennebaker, J.W.: LIWC: Linguistic Inquiry and Word Count. Technical Report. Southern Methodist University, Dallas (1993)Google Scholar
  13. 13.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count. Erlbaum Publishers, Mahwah (2001)Google Scholar
  14. 14.
    Nahar, J., Tickle, K., Ali, S., Chen, P.: Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer. Expert Systems with Applications 39, 12371–12377 (2012)CrossRefGoogle Scholar
  15. 15.
    Chen, L., Qi, L., Wang, F.: Comparison of feature-level learning methods for mining online consumer reviews. Expert Systems with Applications, 9588–9601 (2012)Google Scholar
  16. 16.
    Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Kohavi, R.: A study of cross–validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2(12), pp. 1137–1143. Morgan Kaufmann, San Mateo (1995)Google Scholar
  18. 18.
    Rushdi Saleh, M., Martín Valdivia, M.T., Montejo, A., Ureña, L.A.: Experiments with SVM to classify opinions in different domains. Expert Systems with Applications 38(12), 14799–14804 (2011)CrossRefGoogle Scholar
  19. 19.
    Chen, Y., Lin, C.: Combining SVMs with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction. STUDFUZZ, vol. 207, pp. 315–324. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Peñalver-Martínez, I., Valencia-García, R., García-Sánchez, F.: Ontology-guided approach for Feature-Based Opinion Mining. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 193–200. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Estanislao López-López
    • 1
  • María del Pilar Salas-Zárate
    • 1
  • Ángela Almela
    • 2
  • Miguel Ángel Rodríguez-García
    • 1
  • Rafael Valencia-García
    • 1
  • Giner Alor-Hernández
    • 3
  1. 1.Departamento de Informática y SistemasUniversidad de MurciaMurciaSpain
  2. 2.Department of Modern LanguagesUniversidad Católica San Antonio de MurciaMurciaSpain
  3. 3.Division of Research and Postgraduate Studies Instituto Tecnológico de OrizabaOrizabaMéxico

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