Crisis Response in Tourism: Semantic Networks and Topic Modeling in the Hotel and Aviation Industries

The COVID-19 pandemic caused unprecedented global disruptions, with the hotel and aviation industries—two critical pillars of tourism—among the hardest hit. This study analyzed 451 hotel-related and 336 aviation-related records from the Web of Science database, applying semantic network analysis to uncover eight clusters of crisis management knowledge: basic functions, crisis response, operational strategies, epidemic prevention and control, crisis perception, innovative services, scope of influence, and internal and external environments. Latent Dirichlet Allocation (LDA) topic modeling identified distinct thematic strategies for each sector. In hotels, these included Digital Innovation Transformation, Monitoring Management Procedures, Emotional Awareness Incentives, and Resilience Mechanism Establishment. In aviation, strategies focused on Green Economic Transformation, Co-creation Value Realization, Passenger Incentive Mechanisms, and Balancing Health Risks. By visualizing co-occurrence relationships and mapping thematic intersections and divergences, this study provides actionable insights into the recovery strategies of these industries. The findings offer robust support for developing targeted management approaches and decision-making frameworks to ensure the sustainable growth of the tourism sector.