The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model

Lifeng He, Dongmei Han, Xiaohang Zhou and Zheng Qu
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Lifeng He: School of Information Management and Engineering, Shanghai University of Finance and Economics, No.777 Guoding Road, Shanghai 200433, China
Dongmei Han: School of Information Management and Engineering, Shanghai University of Finance and Economics, No.777 Guoding Road, Shanghai 200433, China
Xiaohang Zhou: School of Information Management and Engineering, Shanghai University of Finance and Economics, No.777 Guoding Road, Shanghai 200433, China
Zheng Qu: School of Information Management and Engineering, Shanghai University of Finance and Economics, No.777 Guoding Road, Shanghai 200433, China

IJERPH, 2020, vol. 17, issue 10, 1-18

Abstract: Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers’ concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

Keywords: online drug review; structural topic model; text mining; consumer concerns (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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