Text mining-based analysis of night tourism perception structure: the case of Han-river park

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Key Points

The aim is to analyze the public's perception structure of night tourism at Han-River Park using text mining techniques.
Used text mining techniques to analyze about 1,000 unstructured texts from online platforms between April 2023 and April 2026.
Conducted frequency analysis, TF-IDF analysis, topic modeling (LDA), and sentiment analysis to understand perceptions.
Utilized Textom for data refining and comprehensive analysis of tourist sentiments and activities.
Identified five key themes in public perception, including emotional experiences and urban infrastructure.
60.71% of sentiments analyzed were positive, indicating an overall favorable perception of night tourism.
Negative sentiments related to crowdedness, parking issues, and discomfort were also reported.

Analyzes public perceptions of night tourism in Han-River Park, suggesting implications for urban tourism strategies.

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Hwang et al. (2026) studied this question.

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