The Korean Journal of Franchise Management (한국프랜차이즈경영연구)

Korea Society of Franchise Management (한국프랜차이즈경영학회)
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Topic Modeling of News Article Related to Franchise Regulation Using LDA

LDA 를 이용한 '프랜차이즈 규제' 관련 뉴스기사 토픽모델링

  • Received : 2022.10.05
  • Accepted : 2022.12.23
  • Published : 2022.12.10
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Abstract

Purpose: In 2020, the franchise industry accomplished a significant growth compared to the previous year, as the number of franchise companies increased by 9.0% while the number of franchise brands increased by 12.5%. Despite growth in size, the Korean franchise industry underwent many negative incidents, such as franchise ownership sales to private equity funds, that led to deterioration of businesses. From this point of view, this study aims to make various proposals to help policy makers develop franchise industry policies by analyzing trends of the current and previous presidential administrations' franchise policies and regulations using newspaper articles. Research design, data and methodology: A total of 7,439 articles registered in Naver API from February 25, 2013 to November 29, 2021 were extracted. Among them, 34 unrelated video articles were deleted, and a total of 7,405 articles from both administrations were used for analysis. The R package was used for word frequency analysis, word clouding, word correlation analysis, and LDA (Latent Dirichlet Allocation) topic modeling. Results: The keyword frequency analysis shows that the most frequently mentioned keywords during the previous administration include 'no-brand', 'major company', 'bill', 'business field', and 'SMEs', and those mentioned during the current administration include 'industry' and 'policy'. As a result of LDA topic modeling, 9 topics such as 'global startups' and 'job creation' from the previous administration, and 10 topics such as 'franchise business' and 'distribution industry' from the current administration were derived. The results of LDAvis showed that the previous administration operated a policy based on mutual growth of large and small businesses rather than hostile regulations in the franchise business, whereas the current administration extended the regulation related to franchise business to the employment sector. Conclusions: The analysis of past two administrations' franchise policy, it can be suggested that franchisors and franchisees may complement each other in developing the Fair Transactions in Franchise Business Act and achieving balanced growth. Moreover, political support is needed for sound development of franchisors. Limitations and future research suggestions are presented at the end of this study.

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References

  1. Arun, R., Suresh, V., Veni Madhavan. C. E., & Narasimha Myrthy, M. N. (2010). On finding the natural number of topics with latent dirichlet allocation: Some observations. In: Zaki M.J., Yu J.X., Ravindran B., Pudi V. (eds) Advances in knowledge discovery and data mining. PAKDD 2010. Lecture notes in computer science, Vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_43
  2. Blei, D. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  3. Blei, D. M., Ng, A. Y., Jordam, M. I (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
  4. Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7-9), 1775-1781. https://doi.org/10.1016/j.neucom.2008.06.011
  5. Choi, Y. H., Ryu, J. H., & Yoem, G. S. (2005). A study on the operational performance and revision direction of the franchise business act. Fair Trade Commission Service Report, Fair Trade Commission.
  6. Chuang, J., Manning, C. D., & Heer, J. (2012). Termite: Visualization techniques for assessing textual topic models. Advanced Visual Interfaces, 12, 21-25.
  7. Chun, T. Y., Lee, D. K., & Park, N. H. (2020). The effect of marketing activities on the brand recognition, brand familiarity, and purchase intention on the SNS of franchise companies. Journal of Asian Finance, Economics and Business, 7(11), 955-966. https://doi.org/10.13106/JAFEB.2020.VOL7.NO11.955
  8. Deveaud, R., SanJuan, E., & Bellot, P. (2014). Accurate and effective latent concept modeling for ad hoc information retrieval. Document Numerique, 17, 61-84. https://doi.org/10.3166/dn.17.1.61-84
  9. FFA(2021). 2021 Franchise Industry Statistics (Report). Seoul: Foodservice & Franchise Agency(FFA).
  10. Griffiths, T., & Steyvers, M. (2004). Finding scientific topics. PNAS, 1(101), 5228-5235. http://ffa.or.kr/61/?q=YToxOntzOjEyOiJrZXl3b3JkX3R5cGUiO3M6MzoiYWxsIjt9&bmode=view&idx=6752304&t=board https://doi.org/10.1073/pnas.0307752101
  11. Jo. H. I., Kim. J. W., & Lee, B. G. (2019). A study on research trends of blockchain using LDA topic modeling: Focusing on United States, China, and South Korea. Journal of Digital Contents Society, 20(7), 1,453-1,460. https://doi.org/10.9728/dcs.2019.20.7.1453
  12. Kim, J. E., & Baek, S. G. (2016). Analysis of issues on the college and university structural reform evaluation using text big data analytics. Asian Journal of Education, 17(3), 409-436. https://doi.org/10.15753/aje.2016.09.17.3.409
  13. Kim, J. Y., Na, H. S., & Park, K. H. (2021). Topic modeling of profit adjustment research trend in Korean accounting. Journal of Digital Convergence, 19(1), 125-139. https://doi.org/10.14400/JDC.2021.19.1.125
  14. Kim, N. G., Lee, D. H. Choi, H. C., & Wong, W. X. S. (2017). Investigations on techniques an applications of text analytics. The Journal of Korean Institute of Communications and Information Sciences, 42(2), 471-492. https://doi.org/10.7840/kics.2017.42.2.471
  15. Korea Franchise Industry Newspaper (2019). [SPECIAL REPORT] Comparison of Korea-US Franchise Business Act Regulations (November 5, 2019 article)
  16. Lee, D. Y., & Yi, H. S. (2021). Exploring methods for determining the appropriate number of topics in LDA: Focusing on perplexity and harmonic mean method. Journal of Educational Evaluation, 34(1), 1-30.
  17. Maeng, S. S., & Jhung, Y. K. (2017). Legal disputes on franchise business transactions and efficient regulation plans. Economic Law Studies, 16(1), 235-256.
  18. Market Economy News (2020). Shut up and regulation?... If the mart needs to live, the 'self-employed' also live (article dated June 20, 2020).
  19. Moon. Y. H., & Choi, J. H. (2018). Content analysis of journal of channel and retailing: Research trends and future directions. Journal of Channel and Retailing, 23(4), 51-73. https://doi.org/10.17657/jcr.2018.10.31.3
  20. Nahm, C. H. (2016). An illustrative application of topic modeling method to a farmers diary. Cross-Cultural Studies, 22(1), 89-135.
  21. National Law Information Center (NLIC) (2021). https://www.law.go.kr/법령/가맹사업진흥에관한법률/(18267,20210615)
  22. National Law Information Center (NLIC) (2021). https://www.law.go.kr/법령/가맹사업거래의공정화에관한법률/(18569,20211207)
  23. Park. J. H., & Oh, H. J. (2017). Comparison of topic modeling methods for analyzing research trends of archives management in Korea: Focused on LDA and HDP. Journal of Korean Library and Information Science Society, 48(4), 235-258. https://doi.org/10.16981/kliss.48.4.201712.235
  24. Seoul Economy Daily (2021). 23 'Affiliation Act Amendment Bill' in Regulatory Strikes Shakes the Affiliate Ecosystem (January 27, 2021 article)
  25. Song, M. (2017). Text Mining. Seoul: Book Publishig Cheonglam.
  26. Yoon, S. U., Kim, M. C. (2020). Topic modeling on fine dust issues using LDA Analysis. Journal of Energy Engineering, 29(2), 23-29. https://doi.org/10.5855/ENERGY.2020.29.2.023