Advanced Analytics in Social Media Data Mining as a Driver of Digital Transformation in Cultural Heritage Tourism: The Case of Lamphun, Thailand

Social media platforms and user-generated content (UGC) have become central to how travelers discover and evaluate cultural destinations. Yet lesser-known second-tier heritage sites remain substantially underrepresented in digital tourism research. This study investigates how Chinese tourists perceive and engage with the intangible cultural heritage (ICH) of Lamphun, Thailand, through UGC collected from three major Chinese social media platforms (WeChat, Douyin, and Rednote) spanning the period from 2019 to 2023. A total of 642 relevant posts were analyzed using a mixed-methods analytical framework comprising VADER-based sentiment analysis, machine learning classification of tourism intention, and TF-IDF-driven thematic clustering. Results indicate an overall predominance of positive sentiment, particularly toward social rituals, festive events, and traditional craftsmanship, with positive sentiment emerging as the strongest predictor of travel intention. Digital engagement metrics, notably likes and favorites, further amplified the impact of intention-bearing content, while thematic clustering revealed four distinct experiential dimensions, with festival and ritual-centered narratives generating the highest sentiment and tourism intention scores. These findings demonstrate the strategic value of integrating advanced UGC analytics into destination marketing frameworks, offering actionable insights for promoting underrepresented cultural heritage destinations within the increasingly competitive global digital tourism landscape.