Ratings meet reviews in the monitoring of online products and services

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.

Keywords:

• control charts
• joint sentiment-topic model
• statistical process control
• user-generated content

Notes

1 http://gibbslda.sourceforge.net/

2 http://www.cs.pitt.edu/mpqa/

Additional information

Funding

This study is funded by the Key Program of the National Natural Science Foundation of China under grants No. 71932006 and No. 71731008.

Notes on contributors

Qiao Liang

Qiao Liang is a Ph.D. student in the Department of Industrial Engineering, Tsinghua University, Beijing, China. She received her B.S. degree in Industrial Engineering from Tsinghua University in 2016. Her research interests include statistical modeling and data analytics for manufacturing and service processes, with a focus on statistical process control based on text analytics.

Kaibo Wang

Kaibo Wang is a Professor in the Department of Industrial Engineering, Tsinghua University, Beijing, China. He received his B.S. and M.S. degrees in Mechatronics from Xi'an Jiaotong University, Xi'an, China, and his Ph.D. in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology, Hong Kong. His research focuses on statistical quality control and data-driven system modeling, monitoring, diagnosis, and control, with a special emphasis on the integration of engineering knowledge and statistical theories for solving problems from the real industry.