Machine Learning Framework for Early Warning of Rating Declines in Tourism Accommodations

Abstract

This study develops an early warning system framework to predict rating declines in tourism accommodations using machine learning with a validated synthetic data approach. The main challenge addressed is that rating decreases on digital platforms are typically identified only after they occur, resulting in delayed managerial responses and negative impacts on business reputation. The proposed predictive framework integrates customer review sentiment analysis, topic modeling, and temporal aggregation to detect early signals of rating decline one period in advance. Synthetic data were constructed based on the characteristics of the public Bali Hotel Reviews dataset and Google Reviews API documentation, followed by comprehensive statistical validation. Model evaluation indicates that the Random Forest algorithm achieves the best performance in predicting rating declines. The results further show that topic probability, average rating, and minimum rating are the most influential predictors. The developed framework provides interpretable and actionable early warnings through an interactive dashboard with minimal infrastructure requirements.