Text Mining Applications for Mobile Banking User Satisfaction: A Systematic Literature Review - Hentati, Ahmed; Jallouli, Rim (Journal of Telecommunications and the Digital Economy, 2025)
As mobile banking gains popularity for financial transactions, research aimed at enhancing user satisfaction has become increasingly important. This paper examines the literature on the application of text-mining methods to extract insights from user-generated content in the context of mobile banking. The objective is to identify the text-mining methods commonly employed and the key factors influencing user satisfaction in mobile banking. A systematic literature review was conducted to identify relevant articles from Google Scholar, Scopus, IEEE Xplore, and ScienceDirect. The results show that sentiment analysis, topic modelling and word cloud are the most widely used methods in the mobile banking context. Furthermore, the findings highlight that the most cited drivers of user satisfaction in mobile banking based on text mining approaches are security, ease of use and software updates. Additionally, the review uncovers gaps in previous research, particularly the underutilization of advanced text mining methods. To address these gaps, this paper establishes a comprehensive framework that consolidates previous findings and provides actionable recommendations for future research. This framework serves as a guide to better understand user satisfaction and to leverage text mining for more effective insights in the evolving landscape of mobile banking.