Research and Trends in Entrepreneurship Utilizing Generative AI and Big Data Analytics
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study uses text mining and generative AI to analyze entrepreneurship research trends since the 2000s to identify research trends, topics, and future research topics in entrepreneurship. Topic modeling is an unsupervised learning method that discovers hidden topics in a set of documents, analyzes word patterns in documents to infer topics, and identifies words that represent each topic. Generative AI is an AI model that generates new data by learning from existing data and generates various types of data such as images, text, and music. The results of topic modeling showed that entrepreneurship, entrepreneurs, social, business, and research are important topics. Keyword analysis using generative AI revealed the following key research themes: events, welfare, gender roles, virtual, startup, education, social media, and female entrepreneurship. The results of this study can be used can provide a basis for recognizing the importance of entrepreneurship not only for entrepreneurs but also for the general public.
Abstract
This study uses text mining and generative AI to analyze entrepreneurship research trends since the 2000s to identify research trends, topics, and future research topics in entrepreneurship. Topic modeling is an unsupervised learning method that discovers hidden topics in a set of documents, analyzes word patterns in documents to infer topics, and identifies words that represent each topic. Generative AI is an AI model that generates new data by learning from existing data and generates various types of data such as images, text, and music. The results of topic modeling showed that entrepreneurship, entrepreneurs, social, business, and research are important topics. Keyword analysis using generative AI revealed the following key research themes: events, welfare, gender roles, virtual, startup, education, social media, and female entrepreneurship. The results of this study can be used can provide a basis for recognizing the importance of entrepreneurship not only for entrepreneurs but also for the general public.
참고문헌 (21)
- Park, S. T., "A study on topic models using LDA and Word2Vec in travel route recommendation: focus on convergence travel and tours reviews", Personal and Ubiquitous Computing, pp.1~17 (2022)
- "2023 Global Entrepreneurship Monitor (GEM) Global Report" (2023)[URL]
- Park, E. M., "A Study on Coffee Research Trends : Focusing on Overseas Research Trends", The journal of Korea Coffee & Culture Study, 9(1), pp.129~152 (2023)
- Cantillon, R., "An essay on commerce in general", McMaster University Archive for the History of Economic Thought, ~
- Schumpeter J. A., "The theory of economic development. An inquiry into profits, capital, credit, interest, and the business cycle" (1983), Transaction Publishers
- Morris, M. H., "The relationship between entrepreneurship and marketing in established firms", Journal of business venturing, 2(3), pp.247~259 (1987)
- Miller, D., "The correlates of entrepreneurship in three types of firms", Management science, 29(7), pp.770~791 (1983)
- Drucker, P. F., "Corporate takeovers-what is to be done?", The Public Interest, 82, pp.3~24 (1986)
- Brandstätter, H., "Personality aspects of entrepreneurship : A look at five meta-analyses", Personality and individual differences, 51(3), pp.222~230 (2011)
- Dumais, S., "Using SVMs for text categorization", IEEE Intelligent Systems, 13(4), pp.21~23 (1998)
- Wang, L. L., "Text mining approaches for dealing with the rapidly expanding literature on COVID-19", Briefings in Bioinformatics, 22(2), pp.781~799 (2021)
- Park, E. M., "Analysis of research trends in technology innovation : Focus on SCOPUS DB", Journal of Convergence for Information Technology, 10(8), pp.120~126 (2020)
- Blei, D. M., "Latent dirichlet allocation", Journal of machine Learning research, 3, pp.993~1022 (2003)
- Griffiths, T., "Finding scientific topics", Proceedings of the National Academy of Sciences of the United States of America, 101(suppl_1), pp.5228~5235 (2004)
- Deerwester, S., "Indexing by latent semantic analysis", Journal of the American society for information science, 41(6), pp.391~407 (1990)
- Pavlik, J. V., "Collaborating with ChatGPT : Considering the implications of generative artificial intelligence for journalism and media education", Journalism & mass communication educator, 78(1), pp.84~93 (2023)
- Aldausari, N., "Video generative adversarial networks : a review", ACM Computing Surveys(CSUR), 55(2), pp.1~25 (2022)
- Li, G., "A study on the service and trend of Fintech security based on text-mining : Focused on the data of Korean online news", Journal of Computer Virology and Hacking Techniques, 13, pp.249~255 (2017)
- Park, S. T., "A study on topic models using LDA and Word2Vec in travel route recommendation : focus on convergence travel and tours reviews", Personal and Ubiquitous Computing, 26, pp.429~445 (2022)
- Ko, M. H., "Mining the open science : Themes and trends 10years of open science", Journal of Advanced Research in Dynamical and Control Systems, 10(1), pp.263~270 (2018)
- Park, S. T., "A study on the trend of cloud service and security through text mining technique", International Journal of Engineering & Technology, 7(2.33), pp.127~132 (2018)