A Knowledge-Based Approach for Polarity Classification in Twitter

被引：19

作者：

Montejo-Raez, Arturo ^{[1
]}

Martinez-Camara, Eugenio ^{[1
]}

Teresa Martin-Valdivia, M. ^{[1
]}

Alfonso Urena-Lopez, L. ^{[1
]}

机构：

[1] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain

来源：

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY | 2014年 / 65卷 / 02期

关键词：

STRENGTH DETECTION; SENTIMENT;

D O I：

10.1002/asi.22984

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：414 / 425

页数：12

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