<p>The hotel industry has been reshaped by the growing influence of online reviews on customer decision-making. Hotel managers respond to customer reviews to improve satisfaction and loyalty, but the impact on customer behavior remains under-researched. This study examines how hotel responses to online reviews influence customer loyalty and return intentions. An online feedback dataset from 50 hotels in Quebec City was collected and analyzed using Aspect-Based Sentiment Analysis (ABSA) to extract key aspects and sentiment polarity. Hotel managers’ responses were classified into three categories using similarity analysis. Machine learning and deep learning models were trained to predict customer return behavior based on sentiment scores, response typologies, and other relevant variables. Results indicate that customer ratings and length of stay significantly influence the likelihood of return. Sentiment scores, as well as the length and type of responses provided by hotel managers, also play a role in customer decisions. Of all tested models, XGBoost performed the best, with a ROC-AUC score of 0.998, an MCC of 0.969, an accuracy of 98%, and an F1-score of 0.98. This study contributes to the understanding of the relationship between hotel responses and customer loyalty by combining a longitudinal approach with ABSA. It identifies the aspects of greatest concern to customers, as well as the most influential variables, using explainability techniques, and helps determine the most effective response type to encourage return visits. The results provide practical recommendations for hotel managers to optimize their response strategies, improve customer loyalty, and attract new customers.</p>