Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features

Abstract

With the development of the e-commerce industry, various brands of products with different qualities and functions continuously emerge, and the number of online shopping users is increasing every year. After purchase, users always leave product comments on the platform, which can be used to help consumers choose commodities and help the e-commerce companies better understand the popularity of their goods. At present, the e-commerce platform lacks an effective way to measure customer satisfaction based on various customer comments features. In this paper, our goal is to build a product customer satisfaction measurement by analyzing the relationship between the important attributes of reviews and star ratings. We first use an improved information gain algorithm to analyze the historical reviews and star rating data to find out the most informative words that the purchasers care about. Then, we make hypotheses about the relevant factors of the usefulness of reviews and verify them using linear regression. We finally establish a customer satisfaction measurement based on different review features. We conduct our experiments based on three products with different brands chosen from the Amazon online store. Based on our experiments, we discover that features such as length and extremeness of the comments will affect the review usefulness, and the consumer satisfaction measurement constructed using the exponential moving average method can effectively reflect the trend of user satisfaction over time. Our work can help companies acquire valuable suggestions to improve product features, increase sales, and help customers make wise purchases.