Journal of the Korean Applied Science and Technology (한국응용과학기술학회지)

The Korean Society of Applied Science and Technology (한국응용과학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Study on the Policy of Supporting University Students in the Beauty Field through Social Big Data Analysis: Based on exploratory data analytics

소셜 빅 데이터 분석을 통한 미용분야 대학생 창업지원 정책에 관한 연구 -탐색적 데이터 분석법을 기반으로-

  • Mi-Yun Yoon (Department of Beauty Care, Pai Chai University ) ;
  • Nam-hoon Park (Grauate School of Thchnology Management, Hanyang University)
  • 윤미연 (배재대학교 뷰티케어학과) ;
  • 박남훈 (한양대학교 기술경영전문대학원)
  • Received : 2022.12.03
  • Accepted : 2022.12.16
  • Published : 2022.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

In order to revitalize start-ups in the beauty field, this study attempted to derive characteristic patterns of changes in demand and differences in emotions and meaning for 'beauty start-ups' by dividing the period by year from 2019 to 2021 based on exploratory data analysis (EDA). Most of the search terms related to the keyword "beauty start-up" showed more interest in institutions or certificates that can learn beauty skills than professional start-up education, which still does not recognize the importance of start-up education, and as an alternative, it is necessary to develop customized start-up education programs for each major. We establish hypotheses through exploratory data analysis and verify hypotheses by combining traditional corroborative data analysis (CDA). There has never been an exploratory data analysis method for beauty startups, and rather than mentioning the need for formal start-up education, analyzing changes in interest in beauty startups and the requirements of prospective start-ups with exploratory data will help develop customized start-up programs.

본 연구에서는 미용분야 창업 활성화를 위해 소셜 빅데이터 분석을 탐색적 데이터 분석(EDA)을 기반으로 하여 2019년부터 2021년 동안 각 년도별로 기간을 구분하여 '미용창업'에 대한 수요 변화와 감정 및 의미 차이의 특징적인 패턴을 도출하고자 하였다. '미용창업' 키워드를 주제로 연관된 검색어를 추출한 결과 창업에 필요한 전문적인 창업교육 보다는 미용관련 기술을 배울 수 있는 기관이나 자격증에 더 많은 관심을 보였으며, 이는 정부 및 지자체에서 여러 가지 창업지원 정책들이 마련되고 있음에도 불구하고 여전히 전문적인 창업교육의 중요성을 인식하지 못하고 있는 것으로 파악할 수 있으며, 이에 대한 대안으로 미용분야 창업을 성공적으로 이루기 위한 전공별 맞춤형 창업교육 프로그램을 개발하는 것이 필요할 것으로 사료된다. 탐색적 데이터 분석을 통해 가설을 설정하고 전통적인 확증적 데이터 분석(CDA)을 결합하여 가설을 검증한다. 미용 창업을 위한 탐색적 데이터 분석 방법이 존재한 적은 없으며, 정식 창업교육의 필요성을 언급하기보다는 미용창업에 대한 관심 변화와 예비창업자의 요구사항을 탐색적 데이터로 분석한다면 맞춤형 창업 프로그램 개발에 도움이 될 것이라고 확신한다.

Keywords

Acknowledgement

이 논문은 2019년 대한민국 교육부와 한국연구재단의 인문사회분야 중견연구자지원사업의 지원을 받아 수행된 연구임(NRF-2019S1A5A2A01046093).

References

  1. T. U. Ahn, T. W. Kang, "A Study on difficulty factors of youth startups for activation local startups", Asia-pacific journal of business venturing and entrepreneurship, Vol.15, No.2, pp.67-80, (2020).  https://doi.org/10.16972/APJBVE.15.2.202004.67
  2. Statistics. (2022) 
  3. Startup Genome. Global startup ecosystem report 2022 [Internet]. Global Entrepreneurship Network, 2022. http://www.startupgenome.com/report/gser2022. (aceessed Jun, 14, 2022) 
  4. G. D. Ahn, T. K. Lee, "A study of facilitating korean youth startup by analyzing the U.S. and Korean university startup ecosystem", Asia-pacific journal of business venturing and entrepreneurship, Vol.33, No.2, pp.104-122, (2018). 
  5. J. H. Lee, "A study on the ways to vitalize students' entrepreneurship", Asia-pacific journal of business venturing and entrepreneurship, Vol.8, No.2, pp.63-74, (2013).  https://doi.org/10.16972/APJBVE.8.2.201306.63
  6. B. Y. Bae, "A study on the relationships of individual entrepreneurial orientation, goal orientation, entrepreneurial motivation of korean college students", Asia-pacific journal of business venturing and entrepreneurship, Vol.16, No.4, pp.59-70, (2021).  https://doi.org/10.16972/APJBVE.16.4.202108.59
  7. W. L. Rice, S. Y. Park, B. Pan, P. Newman, "Forecasting campground demand in US national parks", Annals of Tourism Research, Vol.75, pp.424-439, (2019).  https://doi.org/10.1016/j.annals.2019.01.013
  8. C. H. Choi, Y. W. Yoo, "The Study on the comparative analysis of EFA and CFA", Journal of Digital Convergence, Vol.15, No.10, pp.103-111, (2017).  https://doi.org/10.14400/JDC.2017.15.10.103
  9. J. T. Behrens, "Principles and Procedures of Exploratory Data Analysis", American Psychological Association, Vol.2, No.2, pp.131-160, (1997). 
  10. M. G. Majumder, S. D. Gupta, J. Paul. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis", Journal of Business Research, Vol.150, pp.147-164,(2022).  https://doi.org/10.1016/j.jbusres.2022.06.012
  11. M. N. Chang, Y. S. Heo, H. S. Lim. "Moving to Jeju': An Exploratory Keyword Analysis Using Social Big Data". Journal of Turism & Industry Research, Vol.39, No.1, pp.15-26, (2019). 
  12. J. Leskovec, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets. Cambridge: Cambridge University Press, (2014). 
  13. J. H. Lee, T. H. Um, H. J. Lee, "A Study on Legal Document Classification Systems Using TF-IDF and CNNs". The Journal of Korean Institute of Communications and Information Sciences, pp.982-983, (2019). 
  14. Y. H. Ji,J. H. Han, "A Study on the Exploration of Paradigm and Future Development Plan for Tourism Sector NFT: Focused on TF-IDF and Semantic Network Analysis". Journal of Internet Electronic commerce research. Vol.22, No.2, pp.131-146, (2022). 
  15. H. S. Kim, Y. M. Kim, "A Validation of Effectiveness for Intrusion Detection Events Using TF-IDF". Journal of The Korea Institute of Information Security & Cryptology, Vol.28, No.6, pp.1489-1497, (2018). 
  16. U. Erra, S. Senatore, F. Minnella, G. Caggianese, "Approximate TF-IDF based on topic extraction from massive message stream using the GPU". Information Sciences. Vol.292, No.20, pp.143-161, (2015).  https://doi.org/10.1016/j.ins.2014.08.062
  17. J. Y. Kim,S. J. Jeong, S. J. Lee, "A Study on the Stock Market Prediction Based on Sentiment Analysis of Social Media". Entrue Journal of Information Technology, Vol.13, No.3, pp.59-70, (2014). 
  18. J. A. Lee, J. J. Yang, "A social issue analysis using franchise big data: construction of emotional dictionary through sentimental analysis", Journal of hospitality & tourism studies, Vol.25, No.5, pp.53-61, (2022). 
  19. H. Y. Hwang, K. C. Lee, J. Y. Yu, Y. H. Lee,"Multimodal Sentiment Analysis Using Review Data and Product Information", the journal of society for e-business studies, Vol.27, No.1, pp.15-28, (2022).  https://doi.org/10.7838/JSEBS.2022.27.1.015
  20. Y. Ashima, K. V. Dinesh, "Sentiment analysis using deep learning architectures: a review", Artificial Intelligence Review, Vol.53, pp.4335-4385, (2020).  https://doi.org/10.1007/s10462-019-09794-5
  21. R. Popping, Computer-assisted text analysis, SAGE Publications, Ltd. (2000). 
  22. Y. S. Kim, H. S. Kwon. "An Comparative Study of Articulation on Science Textbook Concepts and Extracted Concepts in Learning Objectives Using Semantic Network Analysis - Focus on Life Science Domain -", Journal of Korean Elementary Science Education. Vol.35, No.3, pp.377-387, (2016).  https://doi.org/10.15267/KESES.2016.35.3.377
  23. G. J. Han, "Significance and Research Tasks of Applying Network Analysis Techniques in Social Science Methodology Situation: Examining Meaning and Network Analysis (Semantic Network Analysis)", Research in Social Studies Education, Vol.10, No.2, pp.219-235, (2003). 
  24. C. S. Park, J. W. Jung, "Text Network Analysis: Detecting Shared Meaning through Socio-cognitive Networks of Policy Stakeholders", Journal of Governmental Studies, Vol.19, No.2, pp.73-108, (2013).