Exploring the antecedents of tourist satisfaction: A big data analysis of ice and snow tourism destinations
- 1Harbin University of Commerce, Harbin, Heilongjiang Province, China
Abstract
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.
Related
- Resource integration spectrum of tourist destinations and its construction: A case study of Zunyi City
- Water quality indices and their impact on coastal pollution and tourist satisfaction: A systematic review
- Tourism urbanization and landscape fragmentation in Taiwan’s Yilan Plain: an integrated analysis of intensity and landscape metrics
- Unraveling critical paths and inequalities in the tourism industry: An integrated water-energy-carbon nexus approach for China
- Determinants of plastic recycling rates in OECD countries: the role of education, tourism, and environmental policies
BibTeX
@article{wang2026exploring,
title = {{Exploring the antecedents of tourist satisfaction: A big data analysis of ice and snow tourism destinations}},
author = {Shuo Wang and Shiyang Chen and Qingyang Guan},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358229},
url = {https://doi.org/10.1371/journal.pone.0358229},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Exploring the antecedents of tourist satisfaction: A big data analysis of ice and snow tourism destinations
AU - Shuo Wang
AU - Shiyang Chen
AU - Qingyang Guan
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0358229
UR - https://doi.org/10.1371/journal.pone.0358229
AB - 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.
SN - 1932-6203
ER -