Recommendation Trust Research of C2C E-commerce Based on Buyer Reviews Text Analysis
（Beijing Information Science & Technology University，Beijing100192，China）
Abstract：Buyer's online reviews are an important source of customers' trust，so it is of great significance to the analysis of the text of buyer's reviews. The authors study from the perspective of commentary factors and recommendation models. On the one hand，based on the mismatch between the three types of comments and the corresponding content of the comments，the authors propose the comment text centrality factor and the comment sentiment factor，consider the transaction time and the amount of money and other factors synthetically，introduce the feedback mechanism，and establish a more comprehensive and objective recommendation model. On the other hand，the model is simulated with algorithm programming. The model can effectively mine the real recommendation value of reviews，improve the reference value of the trust value to the buyer's decision-making，and has high reliability. The results show that in order to improve the buyer's trust，the online platform should not only ensure product quality，but also strive to improve the convenience and credibility of online review information. In addition，buyers' online reviews have an important impact on buyers' purchasing decisions. Buyers need to improve the resolution of online reviews information， thus forming a scientific shopping recommendation and rational shopping decisions. And the supervisors should play their role， create the sound shopping environment，and guarantee the sound order for the development of e-commerce.
Key words：buyers’review；text centrality factor；sentiment factor；recommendation trust