Abstract

User-generated content including both review texts and user ratings provides important information regarding the customer-perceived quality of online products and services. This article proposes a modeling and monitoring method for online user-generated content. A unified generative model is constructed to combine words and ratings in customer reviews based on their latent sentiment and topic assignments, and a two-chart scheme is proposed for detecting shifts of customer responses in dimensions of sentiments and topics, respectively. The proposed method shows superior performance in shift detection, especially for the sentiment shifts in customer responses, based on the results of simulation and a case study.

摘要

用户生成的内容（包括评论文本和用户评分）提供了有关客户感知的在线产品和服务质量的重要信息。本文提出了一种在线用户生成内容的建模和监控方法。构建了一个统一的生成模型，根据潜在情感和主题分配将客户评论中的词和评分结合起来，并提出了一种两图方案，分别用于检测客户在情感和主题维度上的反应变化。基于模拟结果和案例研究，所提出的方法在转移检测方面表现出优异的性能，特别是对于客户响应中的情绪变化。