基于商品特征关联度的购物客户评论可信排序方法

Trustworthy sort method for shopping customer reviews based on correlation degree with product features

电子商务网站中,海量无序的用户评论可能导致消费者客户"迷失"其中,无法识别评论的可信和真假。针对这个问题,提出了一种根据用户评论的可信度对其重新排序的方法。首先,针对网站商品广告信息,关注在线用户评论内容是否和商品功能属性密切相关,设计了基于HTML脚本格式的购物网站中商品关键特征提取算法,给出了基于自然语言处理的用户评论特征词提取方法;然后,利用词语相似度来分析商品特征和用户评论内容之间的关联度,提出了购物客户评论的可信度计算方法;最后,通过实例分析,实现了大量购物客户评论的可信排序,使得用户无须浏览全部或者大部分之后就能判断哪些评价可以信任或者具有实际的参考价值,降低了信息搜索成本,提高了决策效率。

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.