디지털융복합연구 (Journal of Digital Convergence)

  • 제16권9호
  • /
  • Pages.11-20
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  • 2018
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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의미 연결망 분석을 활용한 대학 홈페이지 FAQ 개선방안

Improving University Homepage FAQ Using Semantic Network Analysis

  • 안수현 (세명대학교 교양대학) ;
  • 이상준 (세명대학교 정보통신학부)
  • Ahn, Su-Hyun (College of General Education, Semyung University) ;
  • Lee, Sang-Jun (Dept. of Information & Communication System, Semyung University)
  • 투고 : 2018.06.18
  • 심사 : 2018.09.20
  • 발행 : 2018.09.28
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초록

민원 질의응답의 소통수단으로 보편화된 Q&A 게시판에는 반복된 질문들이 자주 등록되어 민원업무를 효율적으로 관리할 필요성이 제기된다. 본 연구는 대학 홈페이지의 Q&A 게시판에 게재된 비정형 데이터를 중심으로 학생 중심의 질의응답집(FAQ)을 구성하고자 한다. 이에 최근 3년간 690건의 게시물에서 주요 핵심어를 추출하고 의미 연결망 분석을 통해 중심성 분석 및 핵심어 사이의 관계성을 파악하여 네트워크 시각화를 진행하였다. 분석결과 민원질의에서 가장 중심성이 높은 핵심어는 신청, 교과목, 학점, 이수, 졸업, 승인, 기간, 전공, 포털, 학과 등의 순이었다. 또한 주요 핵심어들은 수업, 학적, 학생활동, 장학금, 도서관, 생활관, 정보화, 통학 영역의 8개 군집으로 구분되었다. 이를 토대로 질의횟수가 많은 내용을 분야별로 정리하여 FAQ를 구성한다면 반복적인 질문에 대한 민원응대 프로세스를 간소화함으로써 수요자의 편의성과 행정의 효율성 향상에 기여하고 나아가 대학 구성원간의 원활한 양방향 소통이 가능할 것으로 기대한다.

The Q&A board is widely used as a means of communicating service enquiries, and the need for efficient management of the enquiry system has risen because certain questions are being repeatedly and frequently registered. This study aims to construct a student-centered FAQ, centered on the unstructured data posted on the university homepage's Q&A board. We extracted major keywords from 690 postings registered in the recent 3 years, and conducted the semantic network analysis to find the relationship between the keywords and the centrality analysis in order to carry out network visualization. The most central keywords found through the analysis, in order of centrality, were application, curriculum, credit point, completion, graduation, approval, period, major, portal, department. Also, the major keywords were classified into 8 groups of course, register, student life, scholarship, library, dormitory, IT and commute. If the most frequent questions are organized into these areas to form the FAQ, based on the results above, it is expected to contribute to user convenience and the efficiency of administration by simplifying the service enquiry process for repeated questions, as well as enabling smooth two-way communication among the members of the university.

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참고문헌

  1. B. S. Baek. (2013). The Research on the Problem and Improving Method of Police Civil Affairs Service Offer : Focus on One-Stop Offer. The Journal of Korean Policy Studies. 13(4), 37-54.
  2. E. R. Cho, O. S. Ji, M. G. Hong & S. Y. Choi. (2016). Efficient Handling for Civil Complaints about Transportation Services in Gyeonggi-Do. Gyeonggi Research Institute. 2016(50), 1-76.
  3. J. H. Kim, S. W. Jeong & S. J. Kang. (2018). Chatbot Dialogue System for Academic Affairs using Natural Language Processing and Open Source Software. Asia-Pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 18(3), 907-914.
  4. H. S. Choi. (2003). A Study on the User Guide through Content Analysis of the FAQ at Academic Library Web Site. Journal of the Korean Society for Information Management, 20(2), 219-240. https://doi.org/10.3743/KOSIM.2003.20.2.219
  5. H. J. Choi. (2007). Planning of Public Service Process in the Website of the Central Administrative Organizations for More Effective Service. Proceedings of The KISS Conference, 34(2), 305-310.
  6. M. H. Lee & M. Y. Lee. (2003). A Study on the Electronic Reference Services Using Electronic Bulletin Boards in University Libraries. Journal of the Korean Biblia Society for Library and Information Science, 14(2), 159-178.
  7. S. S Lee. (2014). A Content Analysis of Journal Articles Using the Language Network Analysis Methods. Journal of the Korean Society for Information Management, 31(4), 49-68. https://doi.org/10.3743/KOSIM.2014.31.4.049
  8. D. H. Chung, J. K. Lee, S. E. Kim & K. J. Park. (2013). An Analysis on Congruency between Educational Objectives of Curriculum and Learning Objectives of Textbooks using Semantic Network Analysis. Journal of Korean Earth Science Society, 34(7), 711-726. https://doi.org/10.5467/JKESS.2013.34.7.711
  9. I. W. Jeon, O. J. Jun, M. Y. Choi, H. S. Kim & J. W. Chung. (2017). Characteristics of Civil Complaints to a Local Government based on Social Network Analysis : Focused on Cheonan City E-Bulletin Board (Allso 365). Journal of Regional Studies, 25(2), 117-141.
  10. M. S. Chung, S. H. Park, B. H. Chae & J. Y. Lee. (2017). Analysis of Major Research Trends in Artificial Intelligence through Analysis of Thesis Data. Journal of Digital Convergence, 15(5), 225-233. https://doi.org/10.14400/JDC.2017.15.2.225
  11. J. M. Choe. (2016). Investigating Journal Citation Network with Centrality Measures in the Public Administration and Policy Field. Journal of Digital Convergence, 14(9), 301-308. https://doi.org/10.14400/JDC.2016.14.9.301
  12. S. Y. Jung & K. H. Hwang. (2015). News Frames about Korean Wave and Country Image of Korean and Japan's Major Newspapers : A Semantic Network Analysis for Article Headlines. Korean Journal of Journalism & Communication Studies, 59(3), 300-331.
  13. E. S. Lee & Y. S. Lim. (2012). Exploring Marketing Communication Strategy Using Facebook in South Korea The Semantic Network Analysis of Communication Messages. The Korean Journal of Advertising and Public Relations, 14(3), 124-155.
  14. B. K. Kim, S. B. Jeong & K. S. Kwon. (2013). A Study on Relational Analysis of Purchasing Items of On-line Shopping Mall based on Social Network Analysis. Journal of Digital Convergence, 11(11), 209-217. https://doi.org/10.14400/JDPM.2013.11.11.209
  15. Y. H. Kim. (2015). Semantic Network of Korean Pop Songs - Changing meaning Structure from 1960's to 2000's. Journal of Popular Narative, 21(1), 145-171. https://doi.org/10.18856/jpn.2015.21.1.005
  16. K. H. Chung. (2010). A Study of Foresight Method Based on Textmining and Complexity Network Analysis. Korea Institute of Science & Technology Evaluation and Planning.
  17. S. Wasserman & K. Faust. (1994). Social Network Analysis. Cambridge University Press.
  18. S. S. Lee. (2012). Network Analysis Methods. Nonhyung.
  19. M. L. Doerfel & S. L. Connaughton. (2009). Semantic Networks and Competition: Election Year Winners and Losers in US Televised Presidential Debates, 1960- 2004. Journal of the Association for Information Science and Technology, 60(1), 201-218.
  20. H. C. Kim & K. H. Ahn. (2012). The Relation of Population, Jobs, Social Capitals and Centrality in Seoul Metropolitan Area, using Social Network Theory. Journal of Korea Planning Association, 47(3), 105-122.
  21. D. W. Son. (2005). Social Network Analysis. Kyungmoonsa.
  22. Y. M. Baek. (2017). Text-Mining Using R. Hanulbooks.
  23. J. W. Kim & W. B. Kim. (2016). Semantic Network Approach Toward the Discourses on Emotional Labor. Korean Journal of Sociology, 50(1), 163-188. https://doi.org/10.21562/kjs.2016.02.50.1.163