基于B2C帐号在线评论特征的聚类分析——以京东商城为例

Clustering Analysis of Online Commenting Features Based on B2C Accounts——Taking JD. com as an Example

基于B2C帐号在线评论数据对帐号进行聚类,聚类算法采用EM、SimpleKMeans,并使用基于似然度的聚类算法评估准则作为聚类算法簇个数选取和算法比较依据。最终,选取EM算法并得到5类人群。得结论:站在帐号满意度的层面,京东商城的运营状况基本良好,但是,也有大概十分之一的高级账户存在流失的潜在风险;京东商城正在培育出一批忠诚度高、消费能力旺盛的客户群;评论字数较长的负面评论更易引起关注。

Based on online commenting contents of B2C accounts to make clustering analysis of the accounts, by taking EM and SimpleKMeans as clustering algorithm,by taking likelihood based clustering algorithm estimation criterion as the basis for selecting the number of clustering algorithm clusters and algorithm comparison,five types of people group are generalized by selecting EM algorithm,and the conclusion is that the operating situation of JD. com is good but there is a potential risk for approximate 10 percent high-ranking customer accounts to run off at the level of accounts satisfaction degree,that JD. com is cultivating a group of customers with high-level loyalty and exuberant consumption capacity and that the negative comments with large number of words can attract more concerns.