Exploring the antecedents of tourist satisfaction: A big data analysis of ice and snow tourism destinations.

This study explores tourist satisfaction in winter destinations using a big-data approach that integrates Latent Dirichlet Allocation, sentiment analysis, and Vector Autoregression. Drawing on over 32,000 online reviews from China's ice and snow tourism sites, the research identifies key concerns-such as infrastructure, service quality, and pricing-and reveals strong seasonal and emotional variability. A central finding is the asymmetrical impact of emotion: negative sentiments, especially regarding perceived price unfairness, have a greater influence on satisfaction than positive emotions. The study contributes theoretically by demonstrating the dominance of emotional drivers in satisfaction formation and introducing a scalable, dynamic framework for modeling affective-cognitive interactions over time. These insights highlight the value of emotion-sensitive management strategies in cold-region tourism and offer a methodological foundation for future behavioral research in dynamic tourism contexts.