The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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Topic Based Hierarchical Network Analysis for Entrepreneur Using Text Mining

텍스트 마이닝을 이용한 주제기반의 기업인 네트워크 계층 분석

  • Lee, Donghun (Dept. of Industrial and Management Engineering, Incheon National University) ;
  • Kim, Yonghwa (Dept. of Industrial and Management Engineering, Incheon National University) ;
  • Kim, Kwanho (Dept. of Industrial and Management Engineering, Incheon National University)
  • Received : 2018.05.24
  • Accepted : 2018.07.14
  • Published : 2018.08.31
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Abstract

The importance of convergence activities among business is increasing due to the necessity of designing and developing new products to satisfy various customers' needs. In particular, decision makers such as CEOs are required to participate in networks between entrepreneurs for being connected with valuable convergence partners. Moreover, it is important for entrepreneurs not only to make a large number of network connections, but also to understand the networking relationship with entrepreneurs with similar topic information. However, there is a difficult limit in collecting the topic information that can show the lack of current status of business and the technology and characteristics of entrepreneur in industry sector. In this paper, we solve these problems through the topic extraction method and analyze the business network in three aspects. Specifically, there are C, S, T-Layer models, and each model analyzes amount of entrepreneurs relationship, network centrality, and topic similarity. As a result of experiments using real data, entrepreneur need to activate network by connecting high centrality entrepreneur when the corporate relationship is low. In addition, we confirmed through experiments that there is a need to activate the topic-based network when topic similarity is low between entrepreneurs.

다양한 고객의 요구를 만족시키기 위한 신제품 설계 및 개발의 필요성 때문에 중소기업 간의 융합 활동의 중요성은 증대하고 있다. 특히, 최고 의사결정을 가지는 중소기업 대표는 적합한 융합 활동 파트너를 구하기 위해 인맥관리는 필수적이다. 한편 기업인들은 많은 양의 인맥을 형성하는 것이 중요할 뿐만 아니라 유사한 토픽정보를 가진 기업인과의 인맥관계를 이해하는 것이 중요하다. 그러나 중소기업의 현황 부재와 산업분야별 기업인들의 기술과 특성을 나타낼 수 있는 토픽정보를 수집하는데 어려운 한계가 존재한다. 본 논문에서는 토픽 추출기법을 통해 이와 같은 문제점을 해결하고 3가지 측면에서 기업 네트워크를 분석한다. 구체적으로 C, S, T-Layer 모델이 있으며 각각의 모델은 인맥의 양, 인맥 중심성, 토픽 유사성을 분석한다. 실 데이터를 통한 실험 결과, 인맥의 양이 적은 경우 중심성이 높은 기업과 네트워크를 강화하여 인맥 네트워크를 활성화 시켜야 할 필요가 있고, 토픽 유사성이 낮은 경우 주제 기반의 네트워크를 활성화 시켜야 할 필요가 있다는 것을 실험을 통해 확인하였다.

Keywords

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The Proposed Framework for Entrepreneur Hierarchical Network Analysis

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The Network Structure Obtained Using C-Layer Model

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The Network Structure Obtained using S-layer Model (The value in a node for eachuser represents that user’scentrality)

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The Results of Cumulative Distribution According to Proposed Models

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Network Analysis Results According to Proposed Models

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Examples of Performance Differences the Results According to Proposed Models (Bold and underline means the highest value across models)

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The Results of Proposed Model Score Change and Distribution According to Proposed Models

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The Visualization Results of Top15 Industrial Fields based on S, T-Layer Models

The Overview of Previous Research on Convergence Activities

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Datasets for Learning Color and Word Embedding Models

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Top10 Industrial Fields based on the Numbers of Data Collected

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The Results of Proposed Model Score and Upper Ratios based on C-Layer

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References

  1. Ahn, S. M., Kim, I. W., Choi, B., Cho, Y., Kim. E., and Kim, M-K., "Understanding the Performance of Collaborative Filtering Recommendation through Social Network Analysis," The Journal of Society for e-Business Studies, Vol. 17, No. 2, pp. 129-147, 2012. https://doi.org/10.7838/JSEBS.2012.17.2.129
  2. Company Search, Incheon dream, Bizok, "Http://bizok.incheon.go.kr/," January, 31, 2018.
  3. Kim, H., Park, H., Lim, J., Chul C., and Kim, K., "Function-Property based Causality Network of Patents for Technology Convergence," Proceedings of the Korean Institute of Industrial Engineers, pp. 1205-1219, 2012.
  4. Bores, C., Saurina, C., and Torres, R., "Technological Convergence: a Strategic Perspective," Technovation, Vol. 23, pp. 1-13, 2003. https://doi.org/10.1016/S0166-4972(01)00094-3
  5. Cho, Y. and Kim, E., "A Corporate Strategy on Technological Convergence Through Analyzing Patent Networks and Strategic Indicators," The Journal of Intellectual Property, Vol. 9, No. 4, pp. 191-221, 2014.
  6. Choi, M., "IT R&D Direction for the Age of Convergence," Journal of Communications and Networks, Vol. 25, No. 1, pp. 25-31, 2008.
  7. Cho, Y. and Kim, E., "A Corporate Strategy on Technological Convergence Through Analyzing Patent Networks and Strategic Indicators," The Journal of Intellectual Property, Vol. 9, No. 4, pp. 191-221, 2014.
  8. Greenstein, S. and Khanna, T., "What does Industry Convergence Mean," Competing in the age of digital convergence, pp. 201-226, 1997.
  9. Hacklin, F., Raurich, V., and Marxt, C., "How Incremental Innovation becomes Disruptive: The Case of Technology Convergence," Proceedings of the IEEE International Engineering Management Conference, Vol. 1, pp. 32-36, 2004.
  10. Han, E. J. and Sohn, S. Y., "Technological Convergence in Standards for Information and Communication Technologies," The Journal of Technological Forecasting and Social Change, Vol. 106, pp. 1-10, 2016. https://doi.org/10.1016/j.techfore.2016.02.003
  11. Heo, J. and Yang, C. H., "Applying Network Analysis in Convergent Research Relationships: The Case of High-Tech Convergence Technology Development Program," Journal of Korea Technology Innovation Society, Vol. 16, No. 4, pp. 883-912, 2013.
  12. Jeong, B. K. and Lee, H. Y., "Analyzing the Domestic Collaborative Research Network in Industrial Engineering," Journal of the Korean Institute of Industrial Engineers, Vol. 40, No. 6, pp. 618-627, 2014. https://doi.org/10.7232/JKIIE.2014.40.6.618
  13. Jeong, B. and Lee, H., "Industrial Engineering as a Multidisciplinary Field: Exploring the Structure of Academic Convergence in Industrial Engineering by Journal Citation Network Analysis," Journal of the Korean Institute of Industrial Engineers, Vol. 42, No. 3, pp. 182-197, 2016. https://doi.org/10.7232/JKIIE.2016.42.3.182
  14. Karvonen, M. and Kassi, T., "Patent Citations as a Tool for Analyzing The Early Stages of Convergence," The Journal of Technological Forecasting and Social Change, Vol. 80, No. 6, pp. 1094-1107, 2013. https://doi.org/10.1016/j.techfore.2012.05.006
  15. Kim, E., Cho, Y., and Kim, W., "Dynamic Patterns of Technological Convergence in Printed Electronics Technologies: Patent Citation Network," The Journal of Scientometrics, Vol. 98, No. 2, pp. 975-998, 2014. https://doi.org/10.1007/s11192-013-1104-7
  16. Kim, J., Jang, Y., and Lee, S., "Issues and Efforts for Technology-Humanities Convertgence: Empirical Analysis of Korean Business," Journal of the Korean Institute of Industrial Engineers, Vol. 40, No. 5, pp. 451-461, 2014. https://doi.org/10.7232/JKIIE.2014.40.5.451
  17. Kim, J. and Lee, S., "Issues for Technology-Humanities Convergence: Patent Keyword Analysis," Journal of the Korean Institute of Industrial Engineers, Vol. 42, No. 3, pp. 231-240, 2016. https://doi.org/10.7232/JKIIE.2016.42.3.231
  18. Lee, D. and Kim, K., "WebSite Keyword Selection Method by Considering Semantic Similarity Based on Word2Vec," The Jounal of Society for e-Business Studies, Vol. 23, No. 2, pp. 83-96, 2018.
  19. Lee, H. and Kang, P., "Topic Modeling and Technology Management Research Trend Analysis," Proceedings of the Korean Institute of Industrial Engineers Autumn Conference, pp. 5208-5229, 2016.
  20. Lei, D. T., "Industry Evolution and Competence Development: the Imperatives of Technological Convergence," International Journal of Technology Management, Vol. 19, pp. 699-738, 2000.
  21. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J., "Distributed Representations of Words and Phrases and Their Compositionality," Advances in Neural Information Processing Systems, pp. 3111-3119, 2013.
  22. Park, D. H., Lee, D. H., Kah J. H., and Lee, G. H., "A Study on Tourism Destination Selection of the Inbound Tourist Using Social Network Analysis: Focusing on the Seoul and Gyeongin Tourism Destination," International Journal of Tourism and Hospitality Research, Vol. 29, No. 9, pp. 5-19, 2015.
  23. Rochat, Y., "Closeness Centrality Extended to Unconnected Graphs: The Harmonic Centrality Index," In ASNA 2009.
  24. Wirtz, B. W., "Reconfiguration of Value Chains in Converging Media and Communications Markets," Long Range Planning, Vol. 34, pp. 489-506, 2001. https://doi.org/10.1016/S0024-6301(01)00066-8