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 SnowNLP-based Chinese-language sentiment analysis, rule-based tourism intention classification, and TF-IDF-driven K-means thematic clustering. Results indicate an overall predominance of positive sentiment, with sentiment score emerging as the strongest predictor of tourism intention. Thematic clustering revealed three distinct experiential dimensions, with culinary heritage and contemporary local lifestyle and cafe exploration generating the highest sentiment distribution and within-cluster tourism intention rate. These findings demonstrate the analytical value of integrated UGC data mining for underrepresented ICH destinations and offer empirical insights to support data-driven destination marketing strategies and destination management organization (DMO) decision-making for the promotion of secondary cultural heritage destinations.