A New Feature Selection Method for Text Categorization of Customer Reviews. Issue 4 (20th April 2016)

• Record Type: Journal Article
• Authors: Liu, Miao; Lu, Xiaoling; Song, Jie
• Is Part Of: Communications in statistics. Volume 45:Issue 4(2016)
• Page Start: 1397
• Page End: 1409
• Publication Date: 2016-04-20
• Subjects: Customer reviews -- Feature selection -- Maximizing the difference -- Term document matrix; 62-09; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2013.833227
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract : With the rapid development of e-commerce, online consumer review plays an increasingly important role in consumers' purchase decisions. Most research papers use the quantitative measures of consumer reviews for statistical analysis. Here we focus on analyzing the texts of customer reviews with text mining tools. We propose a new feature selection method called maximizing the difference. Various classification methods such as boosting, random forest and SVM are used to test the performance of the new method along with different evaluation criteria. Both simulation and empirical results show that it improves the effectiveness of the classifier over the existing methods.