文章基本信息

标题：Method for Customer Review Category Based MKL-SVM
本地全文：下载
作者：Dan ZHAO
期刊名称：Management Science and Engineering
印刷版ISSN：1913-0341
电子版ISSN：1913-035X
出版年度：2012
卷号：6
期号：1
页码：64-68
DOI：10.3968/j.mse.1913035X20120601.2850
语种：English
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
关键词：Customer Review;Text Categorization;SVM;Multiple Kernel Learning