出版社：Canadian Research & Development Center of Sciences and Cultures
摘要：Website accumulates a large number of customer reviews for goods and website services. Support vector machine (SVM) is an effective text categorization method, it has strong generalization ability and high classification accuracy which can be used to track and manage customer reviews. But SVM has some weaknesses which slow training convergence speed and difficult to raise the classification accuracy. The paper use heterogeneous kernel functions which have different characteristics to resolve the problem of SVM weak generalization ability to learn and improve the SVM classification accuracy. Through classify customer reviews, online shopping websites resolve issues of critical analysis about mass customers reviews and effectively improve website service standard.Key words: Customer Review; Text Categorization; SVM; Multiple Kernel Learning
其他摘要：Website accumulates a large number of customer reviews for goods and website services. Support vector machine (SVM) is an effective text categorization method, it has strong generalization ability and high classification accuracy which can be used to track and manage customer reviews. But SVM has some weaknesses which slow training convergence speed and difficult to raise the classification accuracy. The paper use heterogeneous kernel functions which have different characteristics to resolve the problem of SVM weak generalization ability to learn and improve the SVM classification accuracy. Through classify customer reviews, online shopping websites resolve issues of critical analysis about mass customers reviews and effectively improve website service standard. Key words : Customer Review; Text Categorization; SVM; Multiple Kernel Learning
