Trustworthy sort method for shopping customer reviews based on correlation degree with product features
Huang Ting-tin · Journal of Computer Applications · 2014
In E-commerce website, massive disorder shopping reviews may make the consumers be lost in the massive shopping reviews and can not distinguish trusted reviews. Therefore, this paper proposed a trustworthy sort method for customer reviews. Firstly, focusing on commercial advertising information in websites and concerning about whether the contents of the online customer reviews and product functional properties are closely related, the authors designed an algorithm of product's key features extractions from shopping websites based on HTML script format, and presented a method of customer reviews features extractions based on natural language processing. Secondly, the authors used the technique of words similarity to analyze the correlation degree between product features and customer reviews contents, and then proposed the computational method of trust degree for shopping customer reviews. Finally, through analyzing the method with an example, the proposed method achieves a trustworthy sort for large online shopping customer reviews. Thus customers need not browse all reviews to judge which one can be trusted or have the real reference value. It decreases information search costs and improves the efficiency of decision making.