This study aims to examine the customer experience with the data obtained from customer reviews for Michelin-starred restaurants. Customer reviews collected from the TripAdvisor platform. The data consists of reviews from customers who have visited 1-Star Michelin restaurants in Türkiye. Sentiment analysis and machine learning techniques were used. Firstly, the obtained customer reviews were cleaned and prepared for analysis. The reviews were categorized by the algorithm as food, atmosphere, service and value and a score was created for each. Then, these categories were included in the analysis as variables. In the study, machine learning algorithms were used to determine the impact of the scores obtained for food, atmosphere, service and value on customer restaurant ratings. The study provides results on how the attributes of food, atmosphere, service and value can shape the customer satisfaction of restaurants. According to the results obtained, food and service are the two most important attributes that enable the restaurant to get a higher rating. This is followed by value. The CHAID algorithm was found to have the highest level of performance. The other algorithms LSVM, neural network and logistic regression performed similarly. This study analyses customer reviews data on Michelin-starred restaurants in depth and reveals the factors that influence the customer experience. The use of machine learning algorithms in studies in this field is limited. This study analyses customer satisfaction by applying machine learning algorithms to the data obtained from customer reviews and reveals in which areas there are expectations for improvement.