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dc.contributor.authorMontejo Ráez, Arturo-
dc.contributor.authorMartínez Cámara, Eugenio-
dc.contributor.authorMartín Valdivia, M. Teresa-
dc.contributor.authorUreña López, L. Alfonso-
dc.identifier.citationMontejo‐Ráez, A., Martínez‐Cámara, E., Martín‐Valdivia, M. T., & Ureña‐López, L. A. (2014). A knowledge‐based approach for polarity classification in T witter. Journal of the Association for Information Science and Technology, 65(2), 414-425.es_ES
dc.description.abstractUntil now, most of the methods published for polarity classification in Twitter have used a supervised approach. The differences between them are only the features selected and the method used for weighting them. In this article, we present an unsupervised method for polarity classification in Twitter. The method is based on the expansion of the concepts expressed in the tweets through the application of PageRank to WordNet. In addition, we integrate SentiWordNet to compute the final value of polarity. The synsets values are weighted with the PageRank scores obtained in the previous random walk process over WordNet. The results obtained show that disambiguation and expansion are good strategies for improving overall performance.es_ES
dc.relation.ispartofJournal of The American Society for Information Science and Technologyes_ES
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 España*
dc.titleA Knowledge based Approach for Polarity Classification in Twitteres_ES
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