Document (#38205)

Author

Montejo-Ráez, A.
Martínez-Cámara, E.
Martín-Valdivia, M.T.
Ureña-López, L.A.

Title

¬A knowledge-based approach for polarity classification in Twitter

Source

Journal of the Association for Information Science and Technology. 65(2014) no.2, S.414-425

Year

2014

Abstract

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.

Object

Twitter

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4. Vilares, D.; Alonso, M.A.; Gómez-Rodríguez, C.: On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages (2015) 0.22

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5. Herdagdelen, A.; Baroni, M.: Stereotypical gender actions can be extracted from web text (2011) 0.22

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