Analysis of Tourism Research Trends Using Big Data: Focused on Topic Modeling

Purpose - This study aims to identify major topics and changes in topics and relationships between topics in tourism big data research by applying BERT-based topic modeling to academic paper titles, and to suggest future research directions and practical implications.Design, data, and methodology - This study collected 4,951 tourism big data-related titles (2019-2023) from Google Scholar, applied BERTopic after preprocessing, and visualized topic clusters using intertopic distance maps to analyze temporal and structural trends.Result - The core topics were digital transformation, performance analytics, consumer experience, and spatial analytics. After COVID-19, the research expanded to include topics such as sustainability, recovery, wellness tourism, and AI. By 2023, the subject structure had become more complex and interconnected, and methodological shifts from machine lear ning to deep learning reflected qualitative changes in tourism big data research.Conclusions - This study confirms that tourism big data research has evolved with technological and socio-environmental changes, driving new research themes and supporting evidence-based policy and strategy development. It also highlights the efficiency of BERTopic over traditional methods and the need for systematic training to effectively implement such techniques in tourism research.