Exploring Post-Experience Evaluation of Gastronomy Tourism Experiences Through Airbnb

This research delves into factors impacting the post-evaluation of Airbnb gastronomic experiences based on expectation disconfirmation theory. Utilizing big data techniques, including topic modeling, sentiment analysis, image analysis, and conventional OLS regression methods, the study scrutinized 196,265 online reviews and 1,331 images from Airbnb Experiences Website. The findings reveal that review sentiments, image vibrancy, host credibility, and the count of reviews play pivotal roles in determining gastronomy experience ratings. Furthermore, Using Latent Dirichlet Allocation, the reviews revealed six main topics in order of importance: food city tours, social interactions, host, local food culture, beverage appreciation, and hands-on learning. These results illuminate the primary considerations of gastronomy tourists, providing guidance for hosts to better design their experiences and offering insights for other stakeholders in this sector.