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Title: Integrating Spanish lexical resources by meta-classifiers for polarity classification
Authors: Martínez Cámara, Eugenio
Martín Valdivia, M. Teresa
M. Dolores, Molina González
José M., Perea Ortega
Abstract: In this paper we focus on unsupervised sentiment analysis in Spanish. The lack of resources for languages other than English, as for example Spanish, adds more complexity to the task. However, we take advantage of some good already existing lexical resources. We have carried out several experiments using different unsupervised approaches in order to compare the different methodologies for solving the problem of the Spanish polarity classification in a corpus of movie reviews. Among all these approaches, perhaps the newest one integrates SentiWordNet with the Multilingual Central Repository to tackle polarity detection directly over the Spanish corpus. However, the results obtained were not as promising as we expected, and so we carried out another group of experiments combining all the methods using meta-classifiers. The results obtained with stacking outperformed the individual experiments and encourage us to continue in this way.
Keywords: Unsupervised polarity detection
Stacking algorithm
Issue Date: 19-May-2014
Citation: Martínez-Cámara, E., Martín-Valdivia, M. T., Molina-González, M. D., & Perea-Ortega, J. M. (2014). Integrating Spanish lexical resources by meta-classifiers for polarity classification. Journal of Information Science, 40(4), 538-554.
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