Structural Evolution and Research Trend Analysis of Nail Cosmetology Using Text Mining

This study analyzes the trends and thematic evolution of research in nail art by text mining techniques. A total of 247 academic articles published between 1999 and 2025 were systematically retrieved from the RISS and KCI databases. The dataset was divided into three phases reflecting legal and industrial transitions: P1 (1999–2014), P2 (2015–2019), and P3 (2020–2025). Quantitative analysis was conducted using multidimensional text mining tools such as KH Coder, VOSviewer, and Voyant Tools, including keyword analysis, self-organizing maps (SOM), correspondence analysis (CA), and multidimensional scaling (MDS). The findings indicate a clear transformation in scholarly orientation across the three phases. P1 was characterized by a multi-nuclear structure emphasizing artistic expression, formative characteristics, and design-centered discourse. P2 demonstrated a consolidated, industry-oriented cluster shaped by the implementation of the national certification system, with increased emphasis on service, consumer satisfaction, and practical training. In P3, research expanded toward digital convergence, data-driven analysis, and emotional consumption, while independent clusters emerged around labor conditions and worker-related issues. Structural visualizations confirmed a shift from artistic exploration to industrial consolidation and subsequently to digitally integrated, consumer-responsive paradigms. This study demonstrates the structural evolution of nail cosmetology research over a 27-year period, highlighting its progression from aesthetic and formative inquiry to industrial formalization and interdisciplinary expansion. The findings contribute to a deeper understanding of the academic development and future directions in nail beauty studies.