Trend Analysis on the Domestic Beauty Industry Using Topic Modeling Techniques

This study analyzed the research trends of the beauty industry from 2000 to 2024. The KCI 621 papers selected by the keyword ‘beauty industry’ were targeted, and the analysis method utilized text mining techniques (TF-IDF analysis, LDA thematic modeling) using Python libraries such as Panda, KoNLPy, Scikit-learn, and Gensim, along with traditional content analysis. As a result, the research on the beauty industry has grown significantly since 2016, confirming that beauty research is the main field. The average citation growth analysis identified the most influential papers, and the main research topics were education, consumer behavior, organizational management, marketing, and brand image. This study provides academic insights linked to ways to revitalize the beauty industry in Korea and methodologically contributes to the objective and in-depth analysis of research trends. In conclusion, this study presents a multifaceted overview of the research on the beauty industry and suggests future research directions. Emphasizing the importance of various academic approaches and sustainability, this study can have a practical impact on government policy and industrial strategy establishment.