论文部分内容阅读
【目的/意义】通过挖掘电子商务平台冗杂的在线评论信息,对在线评论进行效用过滤,将质量高、有用性强的评论呈献给消费者。【方法/过程】从Spearman相关性分析影响在线评论效用高相关因素入手,构建基于模糊神经网络(FNN)的在线商品评论效用模型,提出一种在线评论效用分类方法。【结果/结论】通过对亚马逊电子商务平台手机评论的实例验证,证明本文模型方法能够对在线商品评论效用进行有效区分,提出的在线商品评论分类过滤模型具有较高的准确度和有效性。
【Purpose / Significance】 By digging the complicated online comment information of e-commerce platform, the online comment is filtered by utility, and the high quality and useful comments are presented to the consumers. 【Methods / Procedures】 Starting from Spearman’s correlation analysis, this paper constructs an online product reviewing utility model based on fuzzy neural network (FNN), and proposes an online reviewing utility classification method. [Results / Conclusion] By validating the Amazon e-commerce platform mobile phone reviews, this paper proves that this model method can effectively distinguish the online product reviews utility. The proposed online product reviews classification filter model has high accuracy and effectiveness.