Browse > Article
http://dx.doi.org/10.9708/jksci.2015.20.2.029

A Case Study of a Text Mining Method for Discovering Evolutionary Patterns of Mobile Phone in Korea  

On, Byung-Won (Dept. of Statistics and Computer Science, Kunsan National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.20, no.2, 2015 , pp. 29-45 More about this Journal
Abstract
Systematic theory, concepts, and methodology for the biological evolution have been developed while patterns and principles of the evolution have been actively studied in the past 200 years. Furthermore, they are applied to various fields such as evolutionary economics, evolutionary psychology, evolutionary linguistics, making significant progress in research. In addition, existing studies have applied main biological evolutionary models to artifacts although such methods do not fit to them. These models are also limited to generalize evolutionary patterns of artifacts because they are designed in terms of a subjective point of view of experts who know well about the artifacts. Unlike biological organisms, because artifacts are likely to reflect the imagination of the human will, it is known that the theory of biological evolution cannot be directly applied to artifacts. In this paper, beyond the individual's subjective, the aim of our research is to present evolutionary patterns of a given artifact based on peeping the idea of the public. For this, we propose a text mining approach that presents a systematic framework that can find out the evolutionary patterns of a given artifact and then visualize effectively. In particular, based on our proposal, we focus mainly on a case study of mobile phone that has emerged as an icon of innovation in recent years. We collect and analyze review posts on mobile phone available in the domestic market over the past decade, and discuss the detailed results about evolutionary patterns of the mobile phone. Moreover, this kind of task is a tedious work over a long period of time because a small number of experts carry out an extensive literature survey and summarize a huge number of materials to finally draw a diagram of evolutionary patterns of the mobile phone. However, in this work, to minimize the human efforts, we present a semi-automatic mining algorithm, and through this research we can understand how human creativity and imagination are implemented. In addition, it is a big help to predict the future trend of mobile phone in business and industries.
Keywords
Mobile phone; Evolutionary pattern; Text mining; Visualization;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Arthur, W., "Increasing returns and path dependency in the economy," University of Michigan Press, 1994
2 Basalla, G., "The evolution of technology," Cambridge University Press, 1988
3 Best, M., "Models for interacting populations of memes: Competition and niche behavior," Journal of Memetics, Vol. 1, No. 1, pp.1-9, 1997
4 Blei, D., Ng, A., Jordan, M., and Lafferty, J., "Latent Dirichlet Allocations," Journal of Machine Learning Research, Vol. 3, No. 4-5, pp.993-1022, 2003
5 Blei, D. and Lafferty, J., "Dynamic topic models," International Conference on Machine Learning, New York, USA, 2006
6 Blei, D., "Probabilistic topic models," Communications of the ACM, Vol. 55, No. 4, pp.77-84, 2012   DOI
7 Borshchev, A., and Filippov, A., "From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, and tools," International Conference of System Dynamics Society, July 25-29, Oxford, England
8 Callon, M. and Law, J., "After the individual in society: Lessons on collectivity from science, technology and society," Canadian Journal of Sociology, Vol. 22, No. 2, pp.165-182, 1997   DOI   ScienceOn
9 Cho, H. and Yang, S., "The study on the effects of system quality of smart phone on use of intention," Journal of the Korea Society of Computer and Information, Vol. 16, No. 5, pp. 147-152, 2011   과학기술학회마을   DOI   ScienceOn
10 Choi, J., Baek, Y., and Han, S., "A study of receptive factors of smart phones service from the user's perspective," Journal of the Korea Society of Computer and Information, Vol. 18, No. 11, pp. 181-190, 2013   DOI   ScienceOn
11 Dalvi, N., Kumar, R., Pang, B., and Tomkins, A., "Matching reviews to objects using a language model," Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, August 2009
12 Fei-Fie, L. and Perona, P., "A Bayesian hierarchical model for learning natural science categories," IEEE Computer Vision and Pattern Recognition, 2005
13 Horx, M., "Technolution," 21st Centaury Books, 2009
14 Hughes, T., "Networks of power: Electrification in western society, 1880-1930," Johns Hopkins University Press, 1983
15 Lee, C., Hur, J., Oh, H., Kim, H., Ryu, P., and Kim, H., "Technology trends of issue detection and predictive analysis on social big data," Electronics and Telecommunications Research Institute, 2013
16 Kam, M. and Song, M., "A study on differences of contents and tones of arguments among newspapers using text mining analysis," Journal of Intelligent Information Systems, Vol. 18, No.3, pp.53-77, 2012
17 Kang, S., "Korean lexical analysis," http://nlp.kookmin.ac.kr/HAM/kor/ham-intr.html, 2012
18 Khanafiah, D. and Situngkir, H., "Visualizing the Phylomemetic tree - Innovation as evolutionary process," Journal of Social Complexity, Vol. 2, No. 2, 2006
19 Lee, H., "The evolution of products," Journal of Korean Society of Design Science Vol. 44, 2001, pp.137-146
20 Loewy, R., "Industrial design," Faber & Faber, 1979
21 Mei, Q. and Zhai, C., "Discovering evolutionary theme patterns from text - An exploration of temporal text mining," SIGKDD 2005, August 21-24, Chicago, Illinois, USA
22 Misra, H., Yvon, F., Jose, J., and Cappe, O., "Text segmentation via topic modeling: An analytical study," International Conference on Information and Knowledge Management (CIKM), Hong Kong, China, November 2009
23 Nelson, R. and Winter, S., "An evolutionary theory of economic change," Belknap Press, 1982
24 Oh, H., Choi, Y., Shin, W., Jeong, Y., and Myaeng, S., "Trend properties and a ranking method for automatic trend analysis," Journal of KIISE : Software and Applications Vol. 36, No. 3, 2009, pp.236-243
25 Recorded Future, https://www.recordedfuture.com, 2012
26 Rosen-Zvi, M., Griffiths, T., Steyvers, M., and Smith P., "The author-topic model for authors and documents," Conference on Uncertainty in Artificial Intelligence, AUAI Press, 2004
27 Sun, B., Mitra, P., Zha, H., and Giles, C., "Topic segmentation with shared topic detection and alignment of multiple documents," SIGIR, Amsterdam, Netherlands, July 2007
28 Wagner, C., "Topic models," http://www.slideshare.net/clauwa/topic-models-5274169, 2012
29 JGibbLDA: A Java implementation of Latent Dirichlet Allocation (LDA) using Gibbs sampling for parameter estimation and inference, http://jgibblda.sourceforge.net, 2012