Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features

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 con...

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Bibliographic Details
Main Authors: Yiming Liu, Yinze Wan, Xiaolian Shen, Zhenyu Ye, Juan Wen
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Information
Subjects:
Online Access:https://www.mdpi.com/2078-2489/12/6/234
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spelling doaj-d54cbfe6b9614ee7aa535528d64d4a902021-06-01T01:37:35ZengMDPI AGInformation2078-24892021-05-011223423410.3390/info12060234Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review FeaturesYiming Liu0Yinze Wan1Xiaolian Shen2Zhenyu Ye3Juan Wen4College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaSchool of Finance, Nankai University, Tianjin 300350, ChinaNankai Business School, Nankai University, Tianjin 300071, ChinaSchool of Finance, Nankai University, Tianjin 300350, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaWith 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.https://www.mdpi.com/2078-2489/12/6/234star ratingsreviewshelpfulness ratingcustomer satisfaction evaluationHP filter
collection DOAJ
language English
format Article
sources DOAJ
author Yiming Liu
Yinze Wan
Xiaolian Shen
Zhenyu Ye
Juan Wen
spellingShingle Yiming Liu
Yinze Wan
Xiaolian Shen
Zhenyu Ye
Juan Wen
Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features
Information
star ratings
reviews
helpfulness rating
customer satisfaction evaluation
HP filter
author_facet Yiming Liu
Yinze Wan
Xiaolian Shen
Zhenyu Ye
Juan Wen
author_sort Yiming Liu
title Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features
title_short Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features
title_full Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features
title_fullStr Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features
title_full_unstemmed Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features
title_sort product customer satisfaction measurement based on multiple online consumer review features
publisher MDPI AG
series Information
issn 2078-2489
publishDate 2021-05-01
description 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.
topic star ratings
reviews
helpfulness rating
customer satisfaction evaluation
HP filter
url https://www.mdpi.com/2078-2489/12/6/234
work_keys_str_mv AT yimingliu productcustomersatisfactionmeasurementbasedonmultipleonlineconsumerreviewfeatures
AT yinzewan productcustomersatisfactionmeasurementbasedonmultipleonlineconsumerreviewfeatures
AT xiaolianshen productcustomersatisfactionmeasurementbasedonmultipleonlineconsumerreviewfeatures
AT zhenyuye productcustomersatisfactionmeasurementbasedonmultipleonlineconsumerreviewfeatures
AT juanwen productcustomersatisfactionmeasurementbasedonmultipleonlineconsumerreviewfeatures
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