In modern society, the internet plays a crucial role in collecting tourism-related information. Traditionally, travelers collected tourism information from travel magazines, television, and travel agencies. However, with the widespread use of the internet, these methods of information collection have quickly shifted online. Travelers can easily access real-time, detailed information from official tourist destination websites, travel review sites, and social networking services (SNSs) through the internet. In addition, experiences, photos, and reviews posted by visitors on SNS serve as valuable reference information for other travelers. Such information frequently serves as a key factor in travelers’ decision-making when selecting destinations and planning their trips. Personal experience-based information is conveyed with reliability and familiarity on SNS, making it influential for many people. Therefore, collecting and analyzing tourism-related information from SNS is considered extremely beneficial for promoting tourism. This study uses text mining to analyze data collected from SNS. Furthermore, it is hypothesized that the most critical source of information on SNS is personal experience-based content. Therefore, this research also explores methods for extracting personal review information from the collected data.

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