2026-10-11T13:36:25Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33812875 2026-09-15T17:47:35Z category_272 category_734 category_106 portal_5 item_type_3 month_year_09_2026

<p>Topic coherence scores for candidate LDA models.</p> Shuo Wang (143908) Shiyang Chen (6680855) Qingyang Guan (22788503) Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Science Policy winter destinations using study contributes theoretically sensitive management strategies reveals strong seasonal pricing &# 8212 future behavioral research china &# 8217 000 online reviews snow tourism sites big data analysis sentiment analysis region tourism data approach xlink "> vector autoregression service quality positive emotions negative sentiments modeling affective methodological foundation insights highlight greater influence emotional variability emotional drivers dynamic framework cognitive interactions central finding asymmetrical impact <div><p>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.</p></div> 2026-09-15T17:47:27Z Dataset Dataset 10.1371/journal.pone.0358229.t003 https://figshare.com/articles/dataset/_p_Topic_coherence_scores_for_candidate_LDA_models_p_/33812875 CC BY 4.0