Text Mining Applications for Mobile Banking User Satisfaction A Systematic Literature Review
Main Article Content
Keywords
Mobile banking, text mining, user satisfaction, artificial intelligence, literature review
Abstract
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.
References
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Adebiyi, A. M., & Omotosho, B. S. (2022). Mobile banking applications in Nigeria: An analysis of user sentiments. Journal of Banking, 2022. Available at https://ssrn.com/abstract=4569018
Adiningtyas, H., & Auliani, A. S (2023). Text Mining for Customer Experience Mobile Banking Analysis. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD (pp. 162–165). SciTePress. https://doi.org/10.5220/0012445800003848
Adiningtyas, H., & Auliani, A. S. (2024). Sentiment analysis for mobile banking service quality measurement. Procedia Computer Science, 234, 40–50. https://doi.org/10.1016/j.procs.2024.02.150
Al-Hagree, S., & Al-Gaphari, G. (2022). Arabic Sentiment Analysis Based Machine Learning for Measuring User Satisfaction with Banking Services Mobile Applications: Comparative Study. In 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA) (pp. 1–4). IEEE. https://doi.org/10.1109/eSmarTA56775.2022.9935486
Alismail, M. A., & Albesher, A. S. (2023). Evaluating Developer Responses to App Reviews: The Case of Mobile Banking Apps in Saudi Arabia and the United States. Sustainability, 15(8), 6701. https://doi.org/10.3390/su15086701
Alrizq, M., & Alghamdi, A. (2024). Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniques. Neural Computing and Applications, 36(11), 6005–6023. https://doi.org/10.1007/s00521-023-09400-4
Amalia, A. E., & Nafan, M. Z. (2021). Mining User Reviews for Software Requirements of A New Mobile Banking Application. In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 86–90). IEEE. https://doi.org/10.1109/ISRITI54043.2021.9702813
Asali, A. G. (2021). Social media analysis for investigating consumer sentiment on mobile banking. Journal of International Conference Proceedings, 4(2), 241–253. https://doi.org/10.32535/jicp.v4i2.1247
Bahauddin, A., Ulfah, M., Angin, N. F. B., & Fachrur, A. R. (2024). Improving the quality of mobile banking services using the integration of text mining, mobile banking service quality, and Quality Function Deployment. Journal Industrial Services, 10(1), 117–124. http://dx.doi.org/10.62870/jiss.v10i1.25907
Balcıoğlu, Y. S. (2024). Analyzing Customer Sentiments and Trends in Turkish Mobile Banking Apps: A Text Mining Study. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (80), 49–69. https://doi.org/10.51290/dpusbe.1391631
Basu, B., Sebastian, M. P., & Kar, A. K. (2024). What affects the promoting intention of mobile banking services? Insights from mining consumer reviews. Journal of Retailing and Consumer Services, 77, 103695. https://doi.org/10.1016/j.jretconser.2023.103695
Berru, Y. T., Jimenez, S., Chicaiza, L., & Loayza, V. E. (2024). Emotions and Customer Satisfaction in the Mobile Banking Era: An Empirical Analysis. In Proceedings of the Future Technologies Conference (pp. 247-260). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73125-9_15
Bimantara, M. D., & Zufria, I. (2024). Text Mining Sentiment Analysis On Mobile Banking Application Reviews Using Tf-Idf Method With Natural Language Processing Approach. JINAV: Journal of Information and Visualization, 5(1), 115–123. https://doi.org/10.35877/454RI.jinav2772
Biswas, M., Tania, M. H., Kaiser, M. S., Kabir, R., Mahmud, M., & Kemal, A. A. (2021). ACCU3RATE: A mobile health application rating scale based on user reviews. PloS one, 16(12), e0258050. https://doi.org/10.1371/journal.pone.0258050
Çallı, L. (2023). Exploring mobile banking adoption and service quality features through user-generated content: The application of a topic modeling approach to Google Play Store reviews. International Journal of Bank Marketing, 41(2), 428–454. https://doi.org/10.1108/ijbm-08-2022-0351
Chebil, M., Tobji, M. A. B., & Jallouli, R. (2024). Twitter Opinion Leaders Detection (TOLD): A comprehensive approach. Procedia Computer Science, 246, 1140–1149. https://doi.org/10.1016/j.procs.2024.09.537
Desiraju, K., Mishra, A. N., & Sengupta, P. (2024). Customer perceptions on open banking apps: Insights using Structural topic modeling. Journal of Retailing and Consumer Services, 81, 104029. https://doi.org/10.1016/j.jretconser.2024.104029
Dey, M., Islam, M. Z., & Rana, T. (2023). Applying Text Mining to Understand Customer Perception of Mobile Banking App. In Handbook of Big Data and Analytics in Accounting and Auditing (pp. 309–333). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-4460-4_14
Dinçer, H., Yüksel, S., Canbolat, Z. N., & Pınarbaşı, F. (2020). Data mining-based evaluating the customer satisfaction for the mobile applications: An analysis on Turkish banking sector by using IT2 fuzzy dematel. In Tools and techniques for implementing international E-trading tactics for competitive advantage (pp. 320–339). IGI Global. https://doi.org/10.4018/978-1-7998-0035-4.ch015
Edwina, T. M. (2024). Data-Driven Insights for Mobile Banking App Improvement: A Sentiment Analysis and Topic Modelling Approach for SimobiPlus User Reviews. International Journal of Engineering Trends and Technology, 72(6), 347-360. https://doi.org/10.14445/22315381/IJETT-V72I6P132
Falaiye, T., Elufioye, O. A., Awonuga, K. F., Ibeh, C. V., Olatoye, F. O., & Mhlongo, N. Z. (2024). Financial inclusion through technology: a review of trends in emerging markets. International Journal of Management & Entrepreneurship Research, 6(2), 368–379. https://doi.org/10.51594/ijmer.v6i2.776
Halvadia, N. B., Halvadia, S., & Purohit, R. (2022). Using Text Mining to Identify Key Dimensions of Service Quality for the Indian Public Sector Banks’ Mobile Banking Apps (Preprint). https://doi.org/10.21203/rs.3.rs-1536236/v1
Hentati, A., & Jallouli, R. (2025a). Diagnosing Mobile Banking Applications to Optimize User Experience and Engagement Method, Features and Recommendations. In Bach Tobji, M. A., Jallouli, R., Sadok, H., Lajfari, K., Mafamane, D., & Mahboub, H. (eds) Digital Economy. Emerging Technologies and Business Innovation. ICDEc 2024. Lecture Notes in Business Information Processing, vol. 531. Springer, Cham. https://doi.org/10.1007/978-3-031-76368-7_16
Hentati, A., & Jallouli, R. (2025b). Gamification of Mobile Banking Applications : A Systematic Literature Review. Journal of Telecommunications and the Digital Economy, 13(1), 12–32. https://doi.org/10.18080/jtde.v13n1.1220
Hussain, A., Hannan, A., & Shafiq, M. (2023). Exploring mobile banking service quality dimensions in Pakistan: a text mining approach. International Journal of Bank Marketing, 41(3), 601–618. https://doi.org/10.1108/IJBM-08-2022-0379
Jamadar, J., Karnik, K., Birari, A., & Patil, Y. (2024). User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews. In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (pp. 1–6). IEEE. https://doi.org/10.1109/TQCEBT59414.2024.10545236
Khabour, S. M., Mustafa, D., & AlThebyan, Q. (2023). Arabic Sentiment Analysis of Mobile Banking Services Reviews. In 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 1–8). IEEE. https://doi.org/10.1109/SNAMS60348.2023.10375427
Kim, S., & Ryu, M. H. (2024). Online review based IPA and IPCA: the case of Korean mobile banking apps. International Journal of Bank Marketing, 43(4), 731–756. https://doi.org/10.1108/IJBM-03-2024-0136
Krishnan, J., Bhattacharjee, B., Pratap, M., Yadav, J. K., & Maiti, M. (2024). Survival strategies for family-run homestays: Analyzing user reviews through text mining. Data Science and Management, 7(3), 228-237. https://doi.org/10.1016/j.dsm.2024.03.003
Leem, B. H. (2021). Voice of Mobile Banking Users: A Text Analytics to Explore Customer Complaint Factors. Logos Management Review, 19(3), 21–40. https://doi.org/10.22724/LMR.2021.19.3.21
Leem, B. H., & Eum, S. W. (2021). Using text mining to measure mobile banking service quality. Industrial Management & Data Systems, 121(5), 993–1007. https://doi.org/10.1108/IMDS-09-2020-0545
Lubis, F. A. (2024). User Sentiment Analysis Towards Islamic Banking Applications in Indonesia. Journal of Islamic Economic and Business Research, 4(1), 126–143. https://doi.org/10.18196/jiebr.v4i1.342
Mahmood, T., Naseem, S., Ashraf, R., Asif, M., Umair, M., & Shah, M. (2023). Recognizing factors effecting the use of mobile banking apps through sentiment and thematic analysis on user reviews. Neural Computing and Applications, 35(27), 19885–19897. https://doi.org/10.1007/s00521-023-08827-z
Maulana, F. I., Rahayu, A., Widartha, V. P., Adi, P. D. P., Purnomo, A., & Pramono, A. (2024). A Comparative Study of Machine Learning Models for Sentiment Analysis on User Reviews of the Desa Wisata Nusantara Application in the Google Play Store. In 2024 International Conference on ICT for Smart Society (ICISS) (pp. 1–7). IEEE. https://doi.org/10.1109/ICISS62896.2024.10751661
Misinem, Kurniawan, T. B., Zakaria, M. Z., & Uzailee, M. A. A. (2022). Sentiment Analysis on Users Satisfaction for Mobile Banking Apps in Malaysia. Journal of Data Science, 2022. Retrieved from https://iuojs.intimal.edu.my/index.php/jods/article/view/50
Mohan, L., Mathur, N., & Reddy, Y. R. (2016). Improving mobile banking usability based on sentiments. In Evaluation of Novel Approaches to Software Engineering. 10th International Conference, ENASE 2015, Barcelona, Spain, April 29–30, 2015, Revised Selected Papers 10 (pp. 180–194). Springer International Publishing. https://doi.org/10.1007/978-3-319-30243-0_10
Oh, Y. K., & Kim, J. M. (2022). What improves customer satisfaction in mobile banking apps? An application of text mining analysis. Asia Marketing Journal, 23(4), 3. https://doi.org/10.53728/2765-6500.1581
Okatan, B., & Çam, H. (2024). Analysis of customer reviews for digital banking applications with text mining methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 14(1), 45–60. https://doi.org/10.17714/gumusfenbil.1361431
Omotosho, B. S. (2021). Analysing user experience of mobile banking applications in Nigeria: A text mining approach. CBN Journal of Applied Statistics, 12(1), 77–108. https://doi.org/10.33429/Cjas.12121.4/6
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2025-12-29
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How to Cite
Hentati, A., & Jallouli, R. (2025). Text Mining Applications for Mobile Banking User Satisfaction: A Systematic Literature Review. Journal of Telecommunications and the Digital Economy, 13(4), 148-178. https://doi.org/10.18080/jtde.v13n4.1385
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How to Cite
Hentati, A., & Jallouli, R. (2025). Text Mining Applications for Mobile Banking User Satisfaction: A Systematic Literature Review. Journal of Telecommunications and the Digital Economy, 13(4), 148-178. https://doi.org/10.18080/jtde.v13n4.1385
