Status : Verified
| Personal Name | Paed, Jasmin E. |
|---|---|
| Resource Title | Uncovering Latent Topics from User Reviews of Digital Bank Apps Using Static and Dynamic Topic Modeling |
| Date Issued | July 2026 |
| Abstract | Digital banks have emerged as pivotal drivers of digital finance, addressing issues of financial exclusion through fully digital experiences and incentives like higher interest rates. However, the lack of physical branches poses challenges, especially in customer service, as users often turn to online platforms to voice concerns. Despite the establishment of a digital banking framework five years ago, studies focusing on customer satisfaction in digital banks remain limited. This research addresses this gap by employing static and dynamic topic modeling (DTM) techniques to analyze user reviews from digital banking applications, uncovering latent topics and identifying key feedback and concerns. The study created baseline static topic models using FASTopic and BERTopic. Results showed that BERTopic identified hidden topics in the data that are uniquely specific (e.g., Notification & In-App Ads, Games & Gambling Controls, and Dark Mode Requests). FASTopic, on the other hand, derived broad but balanced topics (e.g., Rewards, Perks & Everyday Utility, Biometric & Password Authentication, App Bugs & Technical Glitches, Transaction Errors, and Smooth Navigation). Although high-level insights were derived from BERTopic, the output of FASTopic was determined to better support the downstream objectives of the study, such as analyzing and characterizing the evolution of topics through time. The study leveraged the output of FASTopic to further isolate and analyze persistent issues of digital banks. The study uncovered trends and patterns for each entity covering both negative and positive reviews. Top concerns or issues were further tracked using the DTM and topic activity over time function. The study translated these trends and patterns into actionable insights per entity, explaining its importance on the overall customer journey. The study extended the analysis to include first quarter 2026 data in order to provide relevant recommendations. With the findings specified in t |
| Degree Course | Professional Master in Data Science (Analytics) |
| Language | English |
| Keyword | Philippines, Digital banks, Topic Modeling, Dynamic Topic Modeling |
| Material Type | Thesis/Dissertation |
Preliminary Pages
28.86 Mb
Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access