여가 심리와 몰입 경험에 대한 빅데이터 텍스트 분석 : 텍스톰을 활용한 탐색적 접근

Abstract

This study aimed to explore how leisure activities influence individuals' psychological well-being and flow experiences by analyzing discourse patterns and attributes using social big data. Focusing on the core keywords “leisure,” “psychology,” and “flow,” the study collected a total of 1,595 data entries from September 1, 2021, to August 31, 2024, across major Korean web portals, including Naver, Google, and Daum. The unstructured text data were processed using the Textom software for text mining and UCINET6 for social network analysis. Text mining techniques such as frequency analysis and Term Frequency–Inverse Document Frequency (TF-IDF) were employed to identify frequently appearing terms and their relative importance. Results showed that keywords such as “flow,” “psychology,” “leisure,” “time,” and “activity” had the highest frequency. Meanwhile, “time,” “game,” “happiness,” and “experience” ranked high in TF-IDF, indicating their contextual significance within the dataset. Furthermore, CONCOR analysis was used to extract word clusters based on relational similarities between terms. This process yielded four meaningful clusters: (1) Reflective Leisure Flow, (2) Psycho-Social Leisure Management, (3) Positive Emotion-Based Flow Experience, and (4) Digital Culture and Leisure Psychology. Each cluster reflects a unique thematic dimension of how leisure and flow are experienced and conceptualized within Korean digital discourse. The findings offer a comprehensive and multidimensional perspective on leisure psychology and flow, highlighting their connections to self-reflection, emotional regulation, and modern digital behaviors. This study provides empirical insights that can serve as a foundation for developing leisure-based psychological intervention programs and evidence-informed leisure policy planning.