All Issue

2022 Vol.55, Issue 12S Preview Page

Research Article

31 December 2022. pp. 1235-1249
Abstract
References
1
Analytic Vidhya (2021). Part 3: Topic Modeling and Latent Dirichlet Allocation (LDA) using Gensim and Sklearn, accessed 1 October 2022, <https://www.analyticsvidhya.com/blog >.
2
Blei, D.M. (2012). “Probabilistic topic models.” Communications of the ACM, Vol. 55, No. 4, pp. 77-84. 10.1145/2133806.2133826
3
Kim, J.S., and Lee, S.J. (2016). “Revisiting the cause of unemployment problem in Korea’s labor market: The job seeker’s interests-based topic analysis.” Management & Information Systems Review, Vol. 35, No. 1, pp. 85-116. 10.29214/damis.2016.35.1.005
4
Kim, S.T., and Lee, C.H. (2007). “A study on the media coverage of public issue: Focusing on drinking-water issues.” Korean Journal of Communication and Information, Vol. 39, pp. 40-68.
5
Lee, S.Y., and Kim, T.J. (2020). “News big data analysis of ‘Tap Water Larvae’ using topic modeling analysis.” The Journal of the Korea Contents Association, Vol. 20, No. 11, pp. 28-37.
6
Ragini, J.R., Anand, P.R., and Bhaskar, V. (2018). “Big data analytics for disaster response and recovery through sentiment analysis.” International Journal of Information Management, Vol. 42, pp. 13-24. 10.1016/j.ijinfomgt.2018.05.004
7
Steyvers, M., and Griffiths, T. (2007). Probabilistic topic models. Handbook of Latent Semantic Analysis. Psychology Press, London, England, pp. 439-460.
8
Zheng, H., Hong, Y., Long, D., and Jing, H. (2017). “Monitoring surface water quality using social media in the context of citizen science.” Hydrology and Earth System Sciences, Vol. 21, No. 2, pp. 949-961. 10.5194/hess-21-949-2017
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 55
  • No :12
  • Pages :1235-1249
  • Received Date : 2022-08-31
  • Revised Date : 2022-10-07
  • Accepted Date : 2022-10-14