面向在线产品评论数据的有效性建模与测度研究.

To analyze online reviews effectively and provide valuable information to both consumers and companies, this paper proposed data modeling and measure system for online product reviews. Firstly, this paper proposed the identifying method based on the KPCA-LS-SVM (kernel principal component analysis least squares support vector machine) model for fake reviews problem. Meanwhile, the paper solved the problem of review data validation analysis by ordinal logistic probability model for the problem of review data validation analysis. At last, experiments were conducted on the real dataset. The results show that it not only can effectively classify fake online reviews, but also improve discriminant validity of the data efficiently. [ABSTRACT FROM AUTHOR]