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Online Shopping Research Trend Analysis Using BERTopic and LDA

  • Received : 2023.01.21
  • Accepted : 2023.02.05
  • Published : 2023.02.28

Abstract

Purpose: As one of the ongoing studies on the distribution industry, the purpose of this study is to identify the research trends on online shopping so far to propose not only the development of online shopping companies but also the possibility of coexistence between online and offline retailers and the development of the distribution industry. Research design, data and methodology: In this study, the English abstracts of 645 papers on online shopping registered in scienceON were obtained. For the analysis through BERTopic and LDA using Python 3.7 and identifying which topics were interesting to researchers. Results: As a result of word frequency analysis and co-occurrence analysis, it was found that studies related to online shopping were frequently conducted on factors such as products, services, and shopping malls. As a result of BERTopic, five topics such as 'service quality' and 'sales strategy' were derived, and as a result of LDA, three topics including 'purchase experience' were derived. It was confirmed that 'Customer Recommendation' and 'Fashion Mall' showed relatively high interest, and 'Sales Strategy' showed relatively low interest. Conclusions: It was suggested that more diverse studies related to the online shopping mall platform, sales content, and usage influencing factors are needed to develop the online shopping industry.

Keywords

References

  1. Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2020). Cross-lingual contextualized topic models with zero-shot learning. arXiv preprint arXiv:2004.07737.
  2. Blei, D. M., & Lafferty, J. D. (2006, June). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (pp. 113-120).
  3. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
  4. Bodrunova, S. S., Orekhov, A. V., Blekanov, I. S., Lyudkevich, N. S., & Tarasov, N. A. (2020). Topic detection based on sentence embeddings and agglomerative clustering with markov moment. Future Internet, 12(9), 144. https://doi.org/10.3390/fi12090144
  5. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  6. Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix factorization with the β-divergence. Neural computation, 23(9), 2421-2456. https://doi.org/10.1162/NECO_a_00168
  7. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
  8. Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent dirichlet allocation(LDA) and topic modeling: Models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169-15211. https://doi.org/10.1007/s11042-018-6894-4
  9. Ju. Y. H., Yang, W. R., & Yang, H. C. (2023). Online shopping research trend analysis using unsupervised learning. Proceedings of the 10th International Forum on Business Convergence(ICFBC2023) of KODISA (pp.141-144). Yangyang, Korea: KODISA.
  10. Jung, M. S., & Shong, H. J. (2020). Effect of online shopping growth on local retail industry: Focusing on Busan region. Bank of Korea Regional Economic Report, Bank of Korea Busan Headquarters. p.113-127.
  11. Kim, S. W., & Yang, K. D. (2022). Topic model augmentation and extension method using LDA and BERTopic. Korean Society for Information Management, 39(3), 99-132.
  12. Kim, T. K., Park, D. J., Choi, I, H., Lee, E. W., & Jang, T. Y. (2018). Ripple effects and implications of online transaction expansion. BOK Issue Note, 2018(10), Bank of Korea. https://www.bok.or.kr/portal/bbs/P0002353/view.do?nttId=10048752&menuNo=200433
  13. Ko, Y. S., Lee, S. B., Cha, M. J., Kim, S. D., Lee, J. H., Ham, J. Y., & Song, M. (2022). Topic modeling insomnia social media corpus using BERTopic and building automatic deep learning classification model. Korean Society for Information Management, 39(2), 111-129.
  14. Korcham (2022). Distribution logistics statistics for 2021. Seoul: Korea Chamber of Commerce and Industry Distribution and Logistics Agency.
  15. Kostat(2022). June 2022 Online Shopping Trends. (http://kostat.go.kr/wnsearch/search.jsp)
  16. Lee, K. B. (2019). A study about the effects of online commerce on the local retail commercial area. Economic Analysis, 25(2), 54-95. https://doi.org/10.23299/BOKERI.2019.25.2.002
  17. Mende, M., & Noble, S. M. (2019). Retail apocalypse or golden opportunity for retail frontline management? Journal of Retailing 95(2), 84-89. https://doi.org/10.1016/j.jretai.2019.06.002
  18. MOTIE (2022). Ministry of Trade, Industry and Energy. Annual '21, December '21 sales trends of major distribution companies. Press release dated January 27, 2022
  19. Park, C. W. (2015). Practical distribution theory. Seoul: Cheongnam Book Publishing House.
  20. Yang, H. C. (2022). Analysis of distribution industry research trends using BERTopic and LDA. Journal of Creativity and Innovation (JCI), 15(4), 71-103.
  21. Yang, W. R., & Yang, H. C. (2022). Topic modeling analysis of social media marketing using BERTopic and LDA. Journal of Industrial Distribution & Business, 13(9), 39-52.