TY - GEN
T1 - Mining Relationships among Online Review Texts and Ratings in Indonesian E-commerce Websites
AU - Hosea, Cindy
AU - Rokhim, Rofikoh
N1 - Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8/22
Y1 - 2020/8/22
N2 - As the most growing sector for Indonesia's internet economy during the last five years, e-commerce generates online customer reviews that can be a source for information and giving hints for potential improvements for various stakeholders. Online reviews consist of review text and rating, each of which describes customer's concern and satisfaction in purchasing items online. However, online review text is unstructured, and its relation with rating is hardly observed. This study examines 132,085 online reviews about Xiaomi mobile phones on three major e-commerce websites in Indonesia: Shopee, Bukalapak, and Blibli by text mining and quantitative modeling to correlate reviews with ratings. Online reviews are classified into eight distinct topics, and the relationships between each topic and review rating are analyzed. Multilinear regression is implemented to examine the valence and strength in the relationship between each topic-rating. The result shows that there are more topics with a negative relationship with rating, with several topic differences between the three websites. Further improvements should be focused on the most impactful topics, which are referred to the mobile phone features, such as CPU & hardware, system, and physical appearance. After-sales service is also concerned at Bukalapak and Blibli. These relationships are explained further by the valence expressed by customers in review texts. The implications of this study can be applied for academic purposes, e-commerce companies, customers, sellers, and mobile phone companies.
AB - As the most growing sector for Indonesia's internet economy during the last five years, e-commerce generates online customer reviews that can be a source for information and giving hints for potential improvements for various stakeholders. Online reviews consist of review text and rating, each of which describes customer's concern and satisfaction in purchasing items online. However, online review text is unstructured, and its relation with rating is hardly observed. This study examines 132,085 online reviews about Xiaomi mobile phones on three major e-commerce websites in Indonesia: Shopee, Bukalapak, and Blibli by text mining and quantitative modeling to correlate reviews with ratings. Online reviews are classified into eight distinct topics, and the relationships between each topic and review rating are analyzed. Multilinear regression is implemented to examine the valence and strength in the relationship between each topic-rating. The result shows that there are more topics with a negative relationship with rating, with several topic differences between the three websites. Further improvements should be focused on the most impactful topics, which are referred to the mobile phone features, such as CPU & hardware, system, and physical appearance. After-sales service is also concerned at Bukalapak and Blibli. These relationships are explained further by the valence expressed by customers in review texts. The implications of this study can be applied for academic purposes, e-commerce companies, customers, sellers, and mobile phone companies.
KW - Big data
KW - E-commerce
KW - Multilinear regression
KW - Online review
KW - Review rating
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85093824465&partnerID=8YFLogxK
U2 - 10.1145/3421537.3421543
DO - 10.1145/3421537.3421543
M3 - Conference contribution
AN - SCOPUS:85093824465
T3 - ACM International Conference Proceeding Series
SP - 11
EP - 15
BT - Proceedings of the 2020 4th International Conference on Big Data and Internet of Things, BDIOT 2020
PB - Association for Computing Machinery
T2 - 4th International Conference on Big Data and Internet of Things, BDIOT 2020
Y2 - 22 August 2020 through 24 August 2020
ER -