Trends Analysis of Korean Anti-aging Research Utilizing Text Mining

This research investigates the evolving landscape of anti-aging research via text mining as a primary analytical approach. A dataset of 93 abstracts from peer-reviewed publications indexed in KCI and RISS was subjected to computational analysis using KH Coder and Voyant Tools, incorporating co-occurrence network analysis, Multidimensional Scaling (MDS), relative frequency change analysis, and Principal Component Analysis (PCA). The analysis identified a tripartite structure of research foci: (1) functional cosmetics efficacy and formulation, (2) genetic and skin regeneration studies, and (3) consumer behavior and marketing strategies related to anti-aging products. Co-occurrence analysis identified ‘beauty,’ ‘skin,’ ‘cosmetic,’ ‘efficacy evaluation,’ ‘marketing,’ and ‘consumer’ as central concepts, with ‘gene’ and ‘regeneration’ exhibiting strong interrelationships, signifying active research in these domains. The MDS analysis showed a shift in research focus from ingredient efficacy toward consumer-oriented marketing research, while PCA corroborated that ‘aging,’ ‘cosmetics,’ and ‘anti-aging’ are the core conceptual pillars in this field. Recent studies have shown a shift from ingredient-focused research toward market-driven consumer behavior studies, highlighting the dynamic nature of the anti-aging industry. These findings underscore the need for ongoing interdisciplinary collaboration to address consumer needs and innovate effectively within anti-aging sector. These insights can benefit skincare brands, researchers, and policymakers by providing insights into emerging anti-aging trends and consumer needs.