참고문헌
- Korea National Statistical Office. Analysis of Consumer Propensity using SNS Data. 2015.
- H. Shim and K. Lim. "Research on the Effect of Different motivations on the Participation in SNSs," Journal of Digital Contents Society, Vol. 12, No. 3, pp. 383-390, 2011. https://doi.org/10.9728/dcs.2011.12.3.383
- Statista. Number of Social Media Users. [Internet] Available:https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/
- Intel. What Happens in an Internet Minute? 2013. Available: https://newsroom.intel.com/press-kits/big-data-intelligence-begins-with-intel/
- A. Oulasvirta, E. Lehtonen, E. Kurvinen,, and M. Raento, "Making the ordinary visible in microblogs," Personal and ubiquitous computing, Vol. 14, No. 3, pp. 237-249, 2010. https://doi.org/10.1007/s00779-009-0259-y
- S. H. Na, J. I. Kim, E. J. Lee, P. K. Kim, "A Study on the Short Text Categorization using SNS Feature Informations," The Journal of Korean Institute of Information Technology, Vol. 14, No. 6, pp. 159-165, June 2016.
- D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet Allocation," Journal of Machine Learning Research, Vol. 3, pp.993-1022, January 2003.
- M. C. Yang and H. C. Rim, "Identifying interesting Twitter contents using topical analysis," Expert Systems with Applications, Vol. 41, No. 9, pp.4330-4336, July 2014. https://doi.org/10.1016/j.eswa.2013.12.051
- C. Xing, Y. Wang, J. Liu, Y. Huang, and W. Y. Ma, "Hashtag-Based Sub-Event Discovery Using Mutually Generative LDA in Twitter," Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 2666-2672, February 2016.
- M. J. Paul and M. Dredze, "Discovering health topics in social media using topic models," PloS one, Vol. 9, No. 8, 2014.
- D. Y. Kim, D. H. Kim, S. W. Kim, M. H. Jo, and E. J. Hwang, "SNS-based issue detection and related news summarization scheme," In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, No. 114, January 2014.
- J. Ito, J. Song, H. Toda, Y. Koike, and S. Oyama, "Assessment of tweet credibility with LDA features," In Proceedings of the 24th International Conference on World Wide Web, pp. 953-958, May 2015.
- Wikipedia, Topic Model, [Internet] Available: https://ko.wikipedia.org/wiki/%ED%86%A0%ED%94%BD_%EB%AA%A8%EB%8D%B8
- D. M. Blei and M. I. Jordan, "Variational inference for Dirichlet process mixtures," Bayesian analysis, Vol. 1, No. 1, pp. 121-143, 2006. https://doi.org/10.1214/06-BA104
- J. S. Liu, "The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem," Journal of the American Statistical Association, Vol. 89, No. 427, pp. 958-966, September 1994. https://doi.org/10.1080/01621459.1994.10476829
- X. Yan, J. Guo, Y. Lan, and X. Cheng, "A biterm topic model for short texts," In Proceedings of the 22nd international conference on World Wide Web, pp. 1445-1456, 2013.
- R. Mehrotra, S. Sanner, W. Buntine, and L. Xie, "Improving lda topic models for microblogs via tweet pooling and automatic labeling," In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp. 889-892, July 2013.
- K. W. Lim, C. Chen, and W. Buntine, "Twitter-network topic model: A full Bayesian treatment for social network and text modeling," NIPS 2013 Topic Models: Computation, Application, and Evaluation, arXiv preprint arXiv:1609.06791, 2016.
- W. Chen, J. Wang, Y. Zhang, H. Yan, and X. Li, "User Based Aggregation for Biterm Topic Model," In ACL, Vol. 2, pp. 489-494, 2015.
- T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
- J. R. Firth, "A synopsis of linguistic theory," Studies in linguistic analysis, pp. 1930-1955, 1957.
- S. Bird, "NLTK: the natural language toolkit," In Proceedings of the COLING/ACL on Interactive presentation sessions, Association for Computational Linguistics, pp. 69-72, July 2006.
피인용 문헌
- Development of Artificial Intelligence-based Legal Counseling Chatbot System vol.26, pp.3, 2018, https://doi.org/10.9708/jksci.2021.26.03.029