With the booming of online shopping, the post-sales online review process becomes a crucial activity in product life cycle management, as it provides abundant user-generated content (UGC) for e-commerce businesses to monitor customer satisfaction, identify potential recalls, and improve electronic Word of Mouth (eWOM). As the core of UGCs, online reviews are generally divided into negative reviews and non-negative reviews in terms of sentiment polarity. Quality issues, such as failures of products or services, are more likely to be hidden in negative reviews. Hence this paper focuses on detecting the abnormal changes of the time-between-review T and sentiment scores S of negative reviews. Due to the complexity and variability of online review processes, the distribution assumptions for S and T may be invalid in real cases. To overcome this problem, we design a distribution-free two-sided monitoring scheme by using the max-type combining function to combine the exponentially weighted moving average (EWMA)-based Wilcoxon rank-sum (WRS) statistics for S and T. The IC and OC performances of the proposed scheme are investigated via simulation study. The results indicate that the proposed scheme outperforms other schemes including a distribution-free EWMA scheme and three parametric Shewhart schemes. Finally, a real example from Ctrip is provided for illustrating the application of the proposed scheme in post-sales service monitoring.

随着在线购物的蓬勃发展，售后在线审查流程成为产品生命周期管理中的关键活动，因为它为电子商务企业提供了丰富的用户生成内容（UGC），以监控客户满意度，识别潜在的召回和改善电子口碑（eWOM）。作为UGC的核心，在线评论通常在情感极性上分为负面评论和非负面评论。产品或服务故障之类的质量问题更可能隐藏在负面评论中。因此，本文着重于检测时间间隔T和情绪得分S的异常变化。负面评论。由于在线审核过程的复杂性和可变性，在实际情况下，S和T的分布假设可能无效。为了解决这个问题，我们通过使用max-type合并函数来组合基于指数加权移动平均（EWMA）的S和T的Wilcoxon秩和（WRS）统计信息，设计了一种无分布的双面监视方案。通过仿真研究了该方案的IC和OC性能。结果表明，所提出的方案优于其他方案，包括无分布EWMA方案和三个参数Shewhart方案。最后，提供了携程旅行网的真实示例，以说明所提出的方案在售后服务监控中的应用。