Analysis of Service Quality in Smart Running Applications Using Big Data Text Mining Techniques

Abstract

In the rapidly evolving digital healthcare market, ensuring both the activation of the market and the fulfillment of the product's social role is essential. This study addresses the service quality of smart running applications by utilizing big data text mining techniques to bridge the gap between user experience and service quality in digital health applications. The research analyzed 264,330 app reviews through sentiment analysis and network analysis, focusing on key service dimensions such as system efficiency, functional fulfillment, system availability, and data privacy. The findings revealed that, while users highly value the functional benefits provided by these applications, there are significant concerns regarding system stability and data privacy. These insights underscore the importance of addressing technical and security issues to enhance user satisfaction and continuous application usage. This study demonstrates the potential of text mining methods in quantifying user experience, offering a robust framework for developing user-centered digital health services. The conclusions emphasize the need for continuous improvement in smart running applications to meet market demands and social expectations, contributing to the broader discourse on the integration of e-commerce and digital health.