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A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies

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  • Yunkai Zhai

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou 450052, China)

  • Xin Song

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou 450052, China)

  • Yajun Chen

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou 450052, China)

  • Wei Lu

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    National Engineering Laboratory for Internet Medical Systems and Applications, Zhengzhou 450052, China)

Abstract

Mobile medicine plays a significant role in optimizing medical resource allocation, improving medical efficiency, etc. Identifying and analyzing user concern elements from active online reviews can help to improve service quality and enhance product competitiveness in a targeted manner. Based on the latent Dirichlet allocation (LDA) topic model, this study carries out a topic-clustering analysis of users’ online comments and builds an evaluation index system of mobile medical users’ satisfaction by using grounded theory. After that, the evaluation information of users is obtained through an emotional analysis of online comments. Then, in order to fully consider the uncertainty of decision makers’ evaluations, rough number theory and the fuzzy comprehensive evaluation method are used to confirm the conclusions of experts and indicators and to evaluate the satisfaction of mobile medical users. The empirical results show that users are satisfied with the service quality and content quality of mobile medical apps, and less satisfied with the management and technology qualities. Therefore, this paper puts forward corresponding countermeasures from the aspects of strengthening safety supervision, strengthening scientific research, strengthening information audit, attaching importance to service quality management and strengthening doctors’ sense of gain. This study uses text mining for index extraction and satisfaction analysis of online reviews to quantitatively evaluate user satisfaction with mobile medical apps, providing a reference for the improvement of mobile medical apps. However, there are still certain shortcomings in the current study, and subsequent studies can screen false reviews for a deeper study of online review information.

Suggested Citation

  • Yunkai Zhai & Xin Song & Yajun Chen & Wei Lu, 2022. "A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies," IJERPH, MDPI, vol. 19(12), pages 1-19, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7466-:d:841700
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    References listed on IDEAS

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    1. Yingyi Zhang & Chengzhi Zhang & Jing Li, 2020. "Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(5), pages 553-567, May.
    2. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    3. Wei Lu & Yunkai Zhai, 2022. "Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback," IJERPH, MDPI, vol. 19(9), pages 1-22, May.
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    Cited by:

    1. Ling Lin & Tao Shu & Han Yang & Jun Wang & Jixian Zhou & Yuxuan Wang, 2023. "Consumer-Perceived Risks and Sustainable Development of China’s Online Gaming Market: Analysis Based on Social Media Comments," Sustainability, MDPI, vol. 15(17), pages 1-20, August.
    2. Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    3. Tao Shu & Zhiyi Wang & Huading Jia & Wenjin Zhao & Jixian Zhou & Tao Peng, 2022. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    4. Zhang, Dianfeng & Shen, Zifan & Li, Yanlai, 2023. "Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).

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