Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10953/2287
Título: A Knowledge based Approach for Polarity Classification in Twitter
Autoría: Montejo Ráez, Arturo
Martínez Cámara, Eugenio
Martín Valdivia, M. Teresa
Ureña López, L. Alfonso
Resumen: Until 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.
Fecha: feb-2014
Editorial: Wiley
Citación: Montejo‐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.
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