Research On Effectiveness Of Online Consumer Reviews Based On IG Extracted Domain Knowledge

The development of Internet and Mobile terminal technology brings great convenience and benefits for the consumers and saved a large amount of money for the sellers as well. However, the online transaction lacks the important part of consumer experience. Online review action as the important complement of product description, which was provided by the electric commercial website owner, and serving for the consumers and retailers. With the development of online shopping users, the hot-sale products and its comments multiple increased, the internet waterborne troops baleful poor assessment and the trash comments and advertisement increased which bring great difficulty for consumers to find out the effectiveness comments for them when make a shopping decision. This paper centre on the effectiveness of online reviews effectiveness and summarized the key factors that affect comments effectiveness, and finally this study put forward the online consumer reviews classification method based on the IG extracted domain knowledge model.At first, the paper sum-up the factors that affect online comments effectiveness and the related technology of comments classification and determined the general research paradigm of this area. Then, this study introduced the method of construct the domain knowledge based on information gain and how to utilize the LibSVM technology to classify online comments. At last, through analysis experimental data of iphone6 s from Amazon, JD and Taobao, the results proved the effectives of the method and we contrast and analysis the differences between the three websites and validate the great influence of the length of comments as well. The experimental results show the great value for the internet owner, sellers and users, and have a significant reference for the development of e-business platform and the study area of the effectiveness of online reviews.