국내 휴대폰의 진화패턴 규명을 위한 텍스트 마이닝 방안 제안 및 사례 연구

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 감미아, "텍스트 마이닝을 활용한 신문사에 따른 내용 및 논조 차이점 분석" 한국지능정보시스템학회 18 (18): 53-77, 2012

2 오흥선, "자동 트렌드 탐지를 위한 속성의 정의 및 트렌드 순위 결정 방법" 한국정보과학회 36 (36): 236-243, 2009

3 조현숙, "스마트폰의 정보시스템 품질이 사용의도에 미치는 영향에 관한 연구" 한국컴퓨터정보학회 16 (16): 147-152, 2011

4 최준혁, "스마트폰 서비스의 수용적 요인에 관한 연구 : 사용자 관점에서" 한국컴퓨터정보학회 18 (18): 181-190, 2013

5 Khanafiah, D., "Visualizing the Phylomemetic tree – Innovation as evolutionary process" 2 (2): 2006

6 Sun, B., "Topic segmentation with shared topic detection and alignment of multiple documents" SIGIR 2007

7 Wagner, C., "Topic models"

8 Basalla, G., "The evolution of technology" Cambridge University Press 1988

9 Lee, H., "The evolution of products" 44 : 137-146, 2001

10 Rosen-Zvi, M., "The author-topic model for authors and documents" AUAI Press 2004

11 Misra, H., "Text segmentation via topic modeling: An analytical study" 2009

12 Horx, M., "Technolution" 21st Centaury Books 2009

13 Lee, C., "Technology trends of issue detection and predictive analysis on social big data" Electronics and Telecommunications Research Institute 2013

14 "Recorded Future"

15 Blei, D., "Probabilistic topic models" 55 (55): 77-84, 2012

16 Hughes, T., "Networks of power: Electrification in western society, 1880-1930" Johns Hopkins University Press 1983

17 Best, M., "Models for interacting populations of memes: Competition and niche behavior" 1 (1): 1-9, 1997

18 Dalvi, N., "Matching reviews to objects using a language model" 2009

19 Blei, D., "Latent Dirichlet Allocations" 3 (3): 993-1022, 2003

20 Kang, S., "Korean lexical analysis"

21 "JGibbLDA: A Java implementation of Latent Dirichlet Allocation (LDA) using Gibbs sampling for parameter estimation and inference"

22 Loewy, R., "Industrial design" Faber & Faber 1979

23 Arthur, W., "Increasing returns and path dependency in the economy" University of Michigan Press 1994

24 Borshchev, A., "From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, and tools"

25 Blei, D., "Dynamic topic models" 2006

26 Mei, Q., "Discovering evolutionary theme patterns from text – An exploration of temporal text mining" 2005

27 Nelson, R., "An evolutionary theory of economic change" Belknap Press 1982

28 Callon, M., "After the individual in society: Lessons on collectivity from science, technology and society" 22 (22): 165-182, 1997

29 Fei-Fie, L., "A Bayesian hierarchical model for learning natural science categories" 2005