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Topic Modeling Analysis of Beauty Industry using BERTopic and LDA

  • Received : 2022.10.11
  • Accepted : 2022.12.05
  • Published : 2022.12.30

Abstract

Purpose: The purpose of this study is identifying the research trends of degree papers related to the beauty industry and providing information which can contribute to the development of the domestic beauty industry and the direction of various research about beauty industry. Research design, data and methodology: This study used 154 academic papers and 189 academic papers with English abstracts out of 299 academic papers. All of these papers were found by searching for the keyword "beauty industry" in ScienceON on August 15, 2022. For the analysis, BERTopic and LDA (Latent Dirichlet Allocation) analysis were conducted using Python 3.7. Also, OLS regression analysis was conducted to understand the annual increase and decrease trend of each topic derived with trend analysis. Results: As a result of word frequency analysis, the frequency of satisfaction, management, behavior, and service was found to be high. In addition, it was found that 'service', 'satisfaction' and 'customer' were frequently associated with program and relationship in the word co-occurrence frequency analysis. As a result of topic modeling, six topics were derived: 'Beauty shop', 'Health education', 'Cosmetics', 'Customer satisfaction', 'Beauty education', and 'Beauty business'. The trend analysis result of each topic confirmed that 'Beauty education' and 'Health education' are getting more attention as time goes by. Conclusions: The future studies must resolve the extreme polarization between the structure of the small beauty industry and beauty stores. Furthermore, the researches have to direct various ways to create the performance of internal personnel. The ways to maximize product capabilities such as competitive cosmetics and brands are also needed attentions.

Keywords

References

  1. Abuzayed, A., & Al-Khalifa, H. (2021). BERT for Arabic topic modeling: An experimental study on BERTopic technique. Procedia Computer Science, 189, 191-194. https://doi.org/10.1016/j.procs.2021.05.096
  2. Bae, K. H., & Lee, Y. J. (2013). Analysis of economic effects of beauty industry by input-output table. The Journal of the Korea Contents Association, 13(4), 350-360. https://doi.org/10.5392/JKCA.2013.13.04.350
  3. Bank of Korea (2008). Industry association table. Seoul: Bank o Korea.
  4. Barua, A., Thomas, S. W., & Hassan, A. E. (2014). What are developers talking about? an analysis of topics and trends in stack overflow. Empirical Software Engineering, 19(3), 619-654. https://doi.org/10.1007/s10664-012-9231-y
  5. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  6. Cho, Y. O., & Youn, C. S. (2017). A study on factor analysis of platform business model in beauty industry using AHP. The Journal of Humanities and Social science, 8(4), 185-206.
  7. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of sciences, 101(suppl_1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  8. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv: 2203.05794.
  9. Health Industry Brief (2021). Basic survey for promotion of cosmetics and beauty service industry and mutual growth plan (vol.325). Seoul: Korea Health Industry Promotion Agency.
  10. Health Industry Trend (2012). 2012 Health industry major performance and issues: Beauty and cosmetic industry. Seoul: Korea Health Industry Promotion Agency.
  11. Kim, J. Y., & Han, C. J. (2021). Influence of beauty service workers' psychological capital on their creative behavior according to empowerment and shared leadership. Journal of The Korean Society of cosmetology, 27(5), 1255-1266. https://doi.org/10.52660/JKSC.2021.27.5.1255
  12. Kim, C. B., & Kim, H. S. (2021). The effect of the quality of education service on the performance of education service through relationship commitment in franchise beauty academy: Moderating effect of trust level. Asia-Pacific Journal of Business Venturing and Entrepreneurship (APJBVE), 16(3), 193-211. https://doi.org/10.16972/APJBVE.16.3.202106.193
  13. Kim, M. S., & Yang, C, K. (2014). A study on the franchise trend and perception of co-branding strategy for beauty industry. Journal of Health and Beauty, 8(2), 35-43.
  14. Maslow, A. H. (1987). Motivation and Personality (3rd ed.). Boston, MA: Addison-Wesley.
  15. Park, J. W., & Kim, S. W. (2017). Effects of SNS wom information characteristics on brand attitude, brand image and purchase intention. Global Business Administration Review, 14(5), 229-249. https://doi.org/10.38115/asgba.2017.14.5.229
  16. Yang, H. C. (2021). Topic modeling analysis of franchise research trends using LDA algorithm. The Korean Journal of Franchise Management, 12(4), 13-23. https://doi.org/10.21871/KJFM.2021.12.5.13
  17. Yang, H. C., & Cho, H. Y. (2022). Topic modeling analysis of HMR research trends using LDA. Korea Logistics Review, 32(1), 81-92.
  18. Yang, H. C., Ju, Y. H., & Cho, H. Y. (2022). Topic modeling of CVS research trends using LDA. The Journal of Business Education, 36(2), 121-143. DOI: 10.34274/krabe.2022.36.2.006
  19. Yang, W. R., & Yang, H. C. (2022a). Topic modeling analysis of social media marketing using BERTopic and LDA. Journal of Industrial Distribution & Business, 13(9), 39-52.
  20. Yang, W. R., & Yang, H. C. (2022b). Overseas research trends telated to 'research ethics' using LDA topic modeling. Journal of Research and Publication Ethics, 3(1), 7-11. https://doi.org/10.15722/JRPE.3.1.202203.7