DOI QR코드

DOI QR Code

Practical Text Mining for Trend Analysis: Ontology to visualization in Aerospace Technology

  • 투고 : 2017.07.04
  • 심사 : 2017.08.14
  • 발행 : 2017.08.31

초록

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

키워드

참고문헌

  1. "K2BASE," Korea Institute of Science & Technology Evaluation and Planning. [Online]. Available: www.k2base.re.kr.
  2. Y. Kim, S. R. Jeong, and I. Ghani, "Text Opinion Mining to Analyze News for Stock Market Prediction," Int. J. Adv. Soft Comput. Its Appl., vol. 6, no. 1, 2014.
  3. W. He, S. Zha, and L. Li, "Social media competitive analysis and text mining: A case study in the pizza industry," Int. J. Inf. Manage., vol. 33, pp. 464-472, 2013. https://doi.org/10.1016/j.ijinfomgt.2013.01.001
  4. Ronen Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2006.
  5. N. F. Noy and D. L. McGuinness, "Ontology Development 101: A Guide to Creating Your First Ontology," Stanford Knowl. Syst. Lab., p. 25, 2001.
  6. Y. Kim, R. Dwivedi, J. Zhang, and S. R. Jeong, "Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5," Online Inf. Rev., vol. 40, no. 1, pp. 42-61, 2016. https://doi.org/10.1108/OIR-03-2015-0068
  7. R. Schumaker and H. Chen, "A discrete stock price prediction engine based on financial news," Computer (Long. Beach. Calif)., no. January, pp. 51-56, 2010.
  8. M. Chau and J. Xu, "Business Intelligence in Blogs: Understanding Consumer Interactions and Communities," MIS Q., vol. 36, no. 4, pp. 1189-1216, 2012.
  9. Z. Zhang, Q. Ye, R. Law, and Y. Li, "The impact of e-word-of-mouth on the online popularity of restaurants: A comparison of consumer reviews and editor reviews," Int. J. Hosp. Manag., vol. 29, no. 4, pp. 694-700, 2010. https://doi.org/10.1016/j.ijhm.2010.02.002
  10. Y. Kim, N. Kim, and S. R. Jeong, "Stock-index Invest Model Using News Big Data Opinion Mining," Journal Intell. Inforamtion Syst., vol. 18, no. 2, pp. 143-156, 2012.
  11. S. Kim, "Research Trends of the Credibility of Information in Social Q&A," J. Korean Soc. Inf. Manag., vol. 29, no. 2, pp. 135-154, 2012. https://doi.org/10.3743/KOSIM.2012.29.2.135
  12. G. Chakraborty, M. Pagolu, and S. Garla, Text Mining and Analysis. 2013.
  13. J.-M. Lee and J.-Y. Rha, "Exploring Consumer Responses to the Cross-Border E-Commerce using Text Mining," J. Consum. Stud., vol. 26, no. 5, pp. 93-124, 2015.
  14. Y. Kim, D. Y. Kwon, and S. R. Jeong, "Comparing Machine Learning Classifiers for Movie WOM Opinion Mining," in Proc. of KSII Trans. Internet Inf. Syst., vol. 9, no. 8, pp. 3178-3190, 2015.
  15. B. Pang and L. Lee, Opinion Mining and Sentiment Analysis. 2008.
  16. M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04, p. 168, 2004.
  17. Y. Kim and S. R. Jeong, "Opinion-Mining Methodology for Social Media Analytics," in Proc. of KSII Trans. Internet Inf. Syst., vol. 9, no. 1, pp. 391-406, 2015.