참고문헌
- LeCun, Y., Bengio, Y., & Hinton, G. "Deep learning," nature, Vol. 521, No. 7553, pp. 436-444, 2015. doi:http://doi.org/10.1038/nature14539
- Amado, A., Cortez, P., Rita, P., & Moro, S. "Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis", European Research on Management and Business Economics, Vol. 24, No. 1, 1-7, 2018. doi:http://doi.org/10.1016/j.iedeen.2017.06.002
- Lee, J. (2019). "A Study on Research Trend Analysis and Topic Class Prediction of Digital Transformation using Text Mining". International journal of advanced smart convergence, 8(2), 183-190. doi:http://doi.org/10.7236/IJASC.2019.8.2.183
- Blei, D.M., “Probabilistic topic models,” Commun. ACM, Vol. 55, No. 4, pp. 77-84, 2012. doi:http://doi.org/10.1145/2133806.2133826
- Steyvers, M. and T. Griffiths, "Probabilistic topic models", Handbook of latent semantic analysis, Vol. 427, No. 7, pp. 424-440, 2007.
- Blei, D.M., A.Y. Ng, and M.I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research", pp. 993-1022, 2003.
- Newman, D., S. Karimi, and L. Cavedon, "External evaluation of topic models," Australasian Doc. Comp. Symp., 2009.
- Bastani, K., Namavari, H., & Shaffer, J. "Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints," Expert Systems with Applications, Vol. 127, pp. 256-271, 2019. doi:http://doi.org/10.1016/j.eswa.2019.03.001
- Shen, D., Wu, G., & Suk, H.-I. "Deep learning in medical image analysis," Annual review of biomedical engineering, Vol. 19, pp. 221-248, 2017. doi:https://doi.org/10.1146/annurev-bioeng-071516-044442