Visualization for University Brand Image Clustering: Comparison between Male and Female Students
Abstract
In this paper, visualization of the results of clustering the brand images of different universities has been presented. Generally high school students care a lot about the brand images of the universities. The brand image is an important criterion for their selection of a particular university to continue their studies. The brand images of universities have been analyzed here by using Bayesian inference. Bayesian inference is widely used in various application fields such as topic extraction. An analysis by topic model has been conducted so far in topic extraction. However, as far as we know, no research that applies the topic model for the clustering of university brand images is available. This is the first application of the topic model in clustering brand images of universities. In this paper, we present the simple topic model visualization tool that we have developed and, as an example, we have applied our developed tool to visualize the clustering of university brand image. When high school students select a university, their university choices greatly depend on the universities’ brand images. So the university public relations section needs the survey of the brand images. The clustering results are helpful for the public relations section of the universities to fix up their publicity strategy. The clustering results show that there is a class of most popular and most difficult universities for high school students and there is another class of public universities which can be characterized by high level education and low cost tuition. The outcome of the clustering is quite consistent with our expectation. In this paper comparison of university choices between male and female students is also presented.
References
P. D. Hoff, A First Course in Bayesian Statistical Methods: Springer, 2010.
M. D. Lee, and E.-J. Wagenmakers, Bayesian Cognitive Modeling: A Practical Course: Cambridge University Press, 2014.
J. Kruschke, Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan: Academic Press, 2014.
Y. Shirota, T. Hashimoto, and B. Chakraborty, "Visual Materials to Teach Gibbs Sampler," International Journal of Knowledge Engineering, vol. 2, pp. 92-95, 2016.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
D. Blei, and J. Lafferty, “Dynamic topic models,” Proceedings of the 23rd International Conference on Machine Learning, 2006.
D. M. Blei, “Probabilistic topic models,” Commun. ACM, vol. 55, no. 4, pp. 77--84, 2013/07/01, 2012.
T. Hashimoto, T. Kuboyama, and Y. Shirota, “Graph-based Consumer Behavior Analysis from Buzz Marketing Sites,” Proc. of 21st European Japanese Conference on Information Modelling and Knowledge Bases, Estonia, June 6-10, 2011.
Y. Shirota, T. Kuboyama, T. Hashimoto, S. Aramvith, and T. Chauksuvanit, Study of Thailand People Reaction on SNS for the East Japan Great Earthquake - Comparion with Japanese People Reaction -, p.^pp. 62: Research Institute for Oriental Cultures Gakushuin University, 2015.
Y. Shirota, T. Hashimoto, and S. Tamaki, “MONETARY POLICY TOPIC EXTRACTION BY USING LDA －JAPANESE MONETARY POLICY OF THE SECOND ABE CABINET TERM －,” Proc. of IIAI International Congress on Advanced Applied Informatics 2015, 12-16 July, 2015, Okayama, Japan, pp. 8-13, 2015.
Y. Shirota, T. Hashimoto, and T. Sakura, "Topic Extraction Analysis for Monetary Policy Minutes of Japan in 2014," Advances in Data Mining: Applications and Theoretical Aspects, Lecture Notes in Computer Science P. Perner, ed., pp. 141-152: Springer International Publishing, 2015.
T. Hashimoto, and Y. Shirota, “Framework of an Advisory Message Board for Women Victims of the East Japan Earthquake Disaster,” Prof. of JADH2013 (Japanese Association for Digital Humanities), Sept 19-21, Kyoto, pp. 31-32, 2013.
Y. Shirota, and B. Chakraborty, “Visual Explanation of Mathematics in Latent Semantic Analysis,” Proc. of IIAI International Congress on Advanced Applied Informatics 2015, 12-16 July, 2015, Okayama, Japan, pp. 423-428, 2015.
Recruit Co., “Survey of University Brand Power 2015,” Recruit Co. College Management, vol. 194 / Sep. - Oct. 2015, pp. 6-44, 2015 (written in Japanese).
C. M. Bishop, Pattern Recognition and Machine Learning: Springer, 2006.
D. Koller, and N. Friedman, Probabilistic Graphical Models: Principles and Techniques: The MIT Press, 2009.
Y. Iba, Bayesian Statistics and Statistical Physics: Iwanami-shoten, 2003, (written in Japanese).
I. Sato, Statistical Latent Semantic Analysis Based on Topic Model: Corona Publishing Co., 2015, (written in Japanese).
D. J. MacKay, Information Theory, Inference, and Learning Algorithms: Cambridge university press, 2003.
A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis, third edition ed.: Chapman & Hall/CRC, 2004.
A. J.-B. Chaney, and D. M. Blei, “Visualizing Topic Models,” Proceedings of the Sixth International Conference on Weblogs and Social Media, Dublin, Ireland, June 4-7, 2012.
