面向供应链的产品评论中客户关注特征挖掘方法研究

Mining Customer Focus Features from Product Reviews Oriented Supply Chain

【目的】针对电子商务平台的中文产品评论,提出一种面向供应链的客户关注特征挖掘方法。【方法】以产品评论数据预处理方法为核心,改进关联规则挖掘产品特征方法。预处理技术包括产品评价概念树、产品评价特征库和MA_Apriori算法。数据实验以京东商城平板电脑为例,在Weka环境中完成客户关注特征的挖掘。【结果】实验表明,对于相同的事务文件,采用数据预处理再进行关联规则的产品特征挖掘,特征查全率为90.5%,而关联规则挖掘方法查全率仅为71.4%。并且本方法可实现产品特征挖掘结果的层次化和规范化。【局限】需要进一步补充汉语分词系统的用户词典,添加产品领域相关的专业词汇,以提高分词准确性。【结论】本方法有助于供应链各节点企业灵活选择产品评价概念层次,从而有针对性地实施产品改进和服务提升。

[Objective] This paper proposes a customer focus feature mining method oriented supply chain. [Methods] The association rule mining is improved by adding data preprocessing, which includes product evaluation conception tree, product evaluation feature database and MA Apriori algorithm. Based on the data of tablet PC of Jingdong Mall, the data experiment mines the customer focus features in Weka. [Results] The experiments show that the recall radio of new method is 90.50, but the association rule method is 71.4%. In addition, it can get the hierarchical and standardized products features. [Limitations] Considering the accuracy of word segmentation, the user dictionary of segmentation system needs to be replenished by adding the product professional vocabulary. [Conclusions] This paper can help each enterprise select the product evaluation conception hierarchies flexibly, then improve the qualities of products and service.