This research project developed an efficient AI framework to analyze big data online reviews. A dataset was collected from social online platforms, like Booking and TripAdvisor. Extension of the present work to other online platforms is straightforward. The BERTopic-based approach was to extract five novel aspects (facility, amenity, service, experience value and loyalty). To enhance construct validity, human-labeled sentiment analysis is combined. Econometric techniques connect aspect-based sentiment with individual ratings within the S-O-R framework. Loyalty-related intentions are strongly associated with higher ratings, which exhibits their mediating role between service delivery and brand equity.
General flowchart of the present work.
Data was collected from Booking and TripAdvisor.
Updated.
- Le, H. T. M., Phan-Thi, T. A., Nguyen, B. T., & Nguyen, T. Q. (2025). Mining online hotel reviews using big data and machine learning: An empirical study from an emerging country. Annals of Tourism Research Empirical Insights, 6(1), 100170. https://doi.org/10.1016/j.annale.2025.100170.
- Le, H. T. M., Nguyen, B. T., & Nguyen, T. Q. (2026). Unlocking insights into customer sentiment analysis: Impact of loyalty on online hotel ratings. International Journal of Hospitality Management, 134, 10457. https://doi.org/10.1016/j.ijhm.2026.104574