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.
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References
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2, 1–135 (2008)
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)
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)
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)
Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences 181, 1138–1152 (2011)
Chen, L., Liu, C., Chiu, H.: A neural network based approach for sentiment classification in the blogosphere. Journal of Informetrics 5, 313–322 (2011)
He, Y., Zhou, D.: Self-training from labeled features for sentiment analysis. Information Processing and Management 47, 606–616 (2011)
Zhai, Z., Xu, H., Kang, B., Jia, P.: Exploiting effective features for chinese sentiment classification. Expert Systems with Applications 38, 9139–9146 (2011)
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)
Stiles, W.B.: Describing talk: A taxonomy of verbal response modes. Sage, Newbury Park (1992)
Pennebaker, J.W., Mayne, T., Francis, M.E.: Linguistic predictors of adaptive bereavement. Journal of Personality and Social Psychology 72, 863–871 (1997)
Francis, M.E., Pennebaker, J.W.: LIWC: Linguistic Inquiry and Word Count. Technical Report. Southern Methodist University, Dallas (1993)
Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count. Erlbaum Publishers, Mahwah (2001)
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)
Chen, L., Qi, L., Wang, F.: Comparison of feature-level learning methods for mining online consumer reviews. Expert Systems with Applications, 9588–9601 (2012)
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)
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)
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)
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)
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)
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López-López, E., del Pilar Salas-Zárate, M., Almela, Á., Rodríguez-García, M.Á., Valencia-García, R., Alor-Hernández, G. (2014). LIWC-Based Sentiment Analysis in Spanish Product Reviews. In: Omatu, S., Bersini, H., Corchado, J., Rodríguez, S., Pawlewski, P., Bucciarelli, E. (eds) Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-07593-8_44
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DOI: https://doi.org/10.1007/978-3-319-07593-8_44
Publisher Name: Springer, Cham
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