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.