Analysis of Beauty Education Research Trends Before and After COVID-19 Using Text Mining Techniques

This study examines shifts in beauty education research trends before (2015-2019) and after (2020-2024) the outbreak of COVID-19, employing text mining to identify significant differences. Research questions were developed to guide the analysis, using TEXTOM for data collection, cleaning, organization, and visualization. Before COVID-19, frequent keywords such as “research,” “education,” and “analysis” highlighted curriculum development, teaching efficiency, and job-focused training. Post-COVID-19, however, keywords shifted to “education,” “research,” and “major,” reflecting an emphasis on remote learning and EduTech as critical adaptations. TF-IDF analysis identified “client” and “NCS” as central pre-pandemic terms, while “microplastics” and “learning” became more prominent post-pandemic, pointing to both educational and social transformations. N-gram analysis revealed a transition from “education-curriculum” and “beauty-education” research themes pre-COVID to “beauty-major” and “non-contact classes” post-COVID, indicating a move towards virtual instruction. Ego network analysis centered on “beauty education” showed that pre-pandemic, core terms included foundational educational concepts, while post-pandemic networks incorporated broader terms like “career” and “learning.” Lastly, CONCOR analysis organized keywords by period, showing pre-COVID clusters centered on in-person training and vocational preparation, whereas post-COVID clusters emphasized development, effectiveness, and satisfaction with online beauty programs. These findings highlight distinct shifts in beauty education research, underscoring areas for ongoing adaptation and future growth in response to changing educational environments.