Earticle

초록

영어

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining—extracting meaningful information from unstructured text data—has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co- occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1 Text Mining and Topic Analysis
  2.2 Clustering Analysis
  2.3 Social Network Analysis
 3. Methodology for Reorganizing ]Social Issues from R&D Perspective Using Social Network Analysis
  3.1 Research Scope
  3.2 Topic Analysis
  3.3 R&D Usage Analysis and Network Merging
  3.4 Quasi-Network Formation and Issue Clustering
 4. System Evaluation
  4.1 Data Description
  4.2 Topic Analysis
  4.3 R&D Usage Analysis and Network Merging
  4.4 Quasi-Network Formation
  4.5 Issue Clustering
 5. Conclusion
 References

키워드

저자

• William Xiu Shun Wong [ Ph.D. Candidate, Graduate School of Business Information Technology, Kookmin University ]
• Namgyu Kim [ Associate Professor, School of Management Information Systems, Kookmin University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005