Proposal: Evaluating User Satisfaction in Mobile Medical Applications Using Text Mining and Sentiment Analysis

Abstract

Mobile medical applications (mHealth apps) have transformed healthcare by offering 
telemedicine, remote monitoring, and digital health services, yet user satisfaction remains inconsistent 
due to issues related to functionality, service quality, technical performance, and security. This study 
proposes a data-driven evaluation model that integrates Latent Dirichlet Allocation (LDA) for topic 
modeling, sentiment analysis for user feedback quantification, and fuzzy comprehensive evaluation 
with rough number theory to assess satisfaction while addressing uncertainties in user perceptions. 
Online reviews from app stores and healthcare forums were analyzed to identify key concerns and 
sentiment trends. Findings indicate that users are satisfied with content quality and service accessibility 
but dissatisfied with technical stability, pricing transparency, and response time. The fuzzy 
comprehensive evaluation yielded an overall satisfaction score of 3.01 out of 5, highlighting the need 
for improvements in system performance, security measures, UI/UX design, and service 
responsiveness. The study offers actionable recommendations for developers, healthcare providers, and 
policymakers to enhance the functionality and user experience of mobile medical applications.