A Big Data Analysis of Aromatherapy Research Trends in Korea Based on Text Mining

This study analyzed trends in Korean aromatherapy research over approximately three decades using text mining. A total of 292 Korean journal articles published from 1994 to March 2026 were retrieved from RISS and analyzed using TEXTOM. Frequency, TF-IDF, Bigram, semantic network, centrality, shortest-path, and CONCOR analyses were conducted across three periods: 1994–2004, 2005–2015, and 2016–2026. The results revealed distinct temporal changes in research topics and network structures. In 1994–2004, studies focused on essential oils, ingredients, efficacy, product development, and component standardization, reflecting an exploratory stage. In 2005–2015, the focus shifted to intervention effects related to stress management, sleep, depression, and healthcare, with more clearly defined populations and clinical contexts. In 2016–2026, data-driven empirical research increased, and topics expanded to cosmetology, wellness, education, physiological responses, and complementary and alternative therapies. Overall, the research structure evolved from material- and efficacy-oriented topics to intervention-, application-, and healing-oriented topics, while network density increased across the three periods. These findings demonstrate the temporal and structural expansion of Korean aromatherapy research into diverse applied fields.