Rattakarn janya and Arinya Pongwat
Published in Data Science and Engineering (DSE) Record 2026 Vol. 7 No. 1 pp. 37-52
Abstract
This independent study aims to analyze user review data and identify key factors influencing user experience (UX) in online travel applications. The study conducts a comparative analysis of three major platforms: Traveloka, Agoda, and Booking.com, using data scraped from the Google Play Store. By leveraging Natural Language Processing (NLP) techniques, the researcher highlights the significance of understanding user needs in the post-pandemic era to enhance digital service efficiency and development. The conceptual framework for categorizing user feedback is based on user experience theories, divided into four primary dimensions: 1) Information Service & Quality, 2) Perceived Benefits, 3) App Performance, and 4) App Design. A dataset comprising 4,035 textual records was collected and subjected to feature extraction for analysis. Experimental results indicate that Logistic Regression outperformed the other evaluated models, including SVM, Neural Network, Naïve Bayes, Random Forest, and Zero-Shot Learning (ZSL), achieving a classification accuracy of 79.14%. Regarding the thematic analysis, "Information Service & Quality" emerged as the most prominent dimension (26.75%), followed by "Perceived Benefits" (25.46%). Furthermore, in-depth visual analytics using Word Clouds and Co-occurrence Networks revealed that negative reviews were significantly associated with keywords such as "Customer," "Service," and "Refund." These findings suggest that service quality and refund processes are pivotal factors in user decision-making. Consequently, this research serves as a strategic guideline for developers to refine functionalities and better meet the evolving demands of contemporary users.