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      기간별 이슈 매핑을 통한 이슈 생명주기 분석 방법론

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      https://www.riss.kr/link?id=A100392717

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      다국어 초록 (Multilingual Abstract)

      Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to...

      Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to create added value. For instance, several attempts are being made to analyze the relevant search keywords that are frequently used on new portal sites and the words that are regularly mentioned on various social media in order to identify social issues. The technique of topic analysis is employed in order to identify topics and themes from a large amount of text documents. As one of the most prevalent applications of topic analysis, the technique of issue tracking investigates changes in the social issues that are identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence.
      However, this traditional issue tracking approach has two limitations. First, when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. This creates practical limitations in the form of significant time and cost burdens. Therefore, this traditional approach is difficult to apply in most applications that need to perform an analysis on the additional period. Second, the issue is not only generated and terminated constantly, but also one issue can sometimes be distributed into several issues or multiple issues can be integrated into one single issue. In other words, each issue is characterized by a life cycle that consists of the stages of creation, transition (merging and segmentation), and termination. The existing issue tracking methods do not address the connection and effect relationship between these issues.
      The purpose of this study is to overcome the two limitations of the existing issue tracking method, one being the limitation regarding the analysis method and the other being the limitation involving the lack of consideration of the changeability of the issues. Let us assume that we perform multiple topic analysis for each multiple period. Then it is essential to map issues of different periods in order to trace trend of issues. However, it is not easy to discover connection between issues of different periods because the issues derived for each period mutually contain heterogeneity.
      In this study, to overcome these limitations without having to analyze the entire periods documents simultaneously, the analysis can be performed independently for each period. In addition, we performed issue mapping to link the identified issues of each period. An integrated approach on each details period was presented, and the issue flow of the entire integrated period was depicted in this study. Thus, as the entire process of the issue life cycle, including the stages of creation, transition (merging and segmentation), and extinction, is identified and examined systematically, the changeability of the issues was analyzed in this study. The proposed methodology is highly efficient in terms of time and cost, as it sufficiently considered the changeability of the issues. Further, the results of this study can be used to adapt the methodology to a practical situation. By applying the proposed methodology to actual Internet news, the potential practical applications of the proposed methodology are analyzed. Consequently, the proposed methodology was able to extend the period of the analysis and it could follow the course of progress of each issues life cycle. Further, this methodology can facilitate a clearer understanding of complex social phenomena using topic analysis.

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      참고문헌 (Reference)

      1 박자현, "토픽모델링을 활용한 국내 문헌정보학 연구동향 분석" 한국정보관리학회 30 (30): 7-32, 2013

      2 배정환, "토픽 모델링을 이용한 트위터 이슈 트래킹 시스템" 한국지능정보시스템학회 20 (20): 109-122, 2014

      3 배정환, "텍스트 마이닝을 이용한2012년 한국대선 관련 트위터 분석" 한국지능정보시스템학회 19 (19): 141-156, 2013

      4 유은지, "시맨틱 텍스트 마이닝을 위한 온톨로지 활용 방안" 한국정보시스템학회 21 (21): 137-161, 2012

      5 홍진성, "단일 카테고리 문서의 다중 카테고리 자동확장 방법론" 한국지능정보시스템학회 20 (20): 77-92, 2014

      6 김지은, "다계층 이원 네트워크를 활용한 사용자관점의 이슈 클러스터링" 한국지능정보시스템학회 20 (20): 93-107, 2014

      7 홍진성, "국가 현안 주제 선정을 위한 데이터 분석 기반 하이브리드 방법론" 엘지씨엔에스 13 (13): 97-111, 2014

      8 Jin, H., "Topic Tracking for Radio, TV Broadcast and Newswire" 1999

      9 Witten, I. H., "Text Mining: Practical Handbook of Internet Computing" CRC Press 2005

      10 Fan, W., "Tapping the Power of Text Mining" 49 (49): 76-82, 2006

      1 박자현, "토픽모델링을 활용한 국내 문헌정보학 연구동향 분석" 한국정보관리학회 30 (30): 7-32, 2013

      2 배정환, "토픽 모델링을 이용한 트위터 이슈 트래킹 시스템" 한국지능정보시스템학회 20 (20): 109-122, 2014

      3 배정환, "텍스트 마이닝을 이용한2012년 한국대선 관련 트위터 분석" 한국지능정보시스템학회 19 (19): 141-156, 2013

      4 유은지, "시맨틱 텍스트 마이닝을 위한 온톨로지 활용 방안" 한국정보시스템학회 21 (21): 137-161, 2012

      5 홍진성, "단일 카테고리 문서의 다중 카테고리 자동확장 방법론" 한국지능정보시스템학회 20 (20): 77-92, 2014

      6 김지은, "다계층 이원 네트워크를 활용한 사용자관점의 이슈 클러스터링" 한국지능정보시스템학회 20 (20): 93-107, 2014

      7 홍진성, "국가 현안 주제 선정을 위한 데이터 분석 기반 하이브리드 방법론" 엘지씨엔에스 13 (13): 97-111, 2014

      8 Jin, H., "Topic Tracking for Radio, TV Broadcast and Newswire" 1999

      9 Witten, I. H., "Text Mining: Practical Handbook of Internet Computing" CRC Press 2005

      10 Fan, W., "Tapping the Power of Text Mining" 49 (49): 76-82, 2006

      11 Albright, R., "Taming Text with the SVD" SAS Institute Inc. 2004

      12 Metzler, D., "Similarity Measures for Tracking Information Flow" 517-524, 2005

      13 Ma, J., "Research on Method of Adaptive Topic Tracking Based on Evolution of Public Opinion Ontology" 4 (4): 1-10, 2014

      14 Song, S. M., "Offering system for major article Using Text Mining and Data Mining" 733-734, 2009

      15 Mooney, R. J., "Mining Knowledge from Text using Information Extraction" 7 (7): 3-10, 2005

      16 Rijsbergen, C. J. V., "Information Retrieval" Butterworths 1979

      17 Gartner, "Hype Cycle for Emerging Technologies" Gartner Inc. 2012

      18 Weiss, S. M., "Fundamentals of Predictive Text Mining" Springer 2010

      19 Ding, W., "Dynamic topic detection and tracking: A comparison of HDP, C-word, and cocitation methods" 65 (65): 2084-2097, 2014

      20 Han, J., "Data Mining: Concepts and Techniques" Morgan Kaufmann Publishers 2011

      21 Sebastiani, F., "Classification of Text, Automatic, The Encyclopedia of Language and Linguistics 14" Elsevier Science Pub 2006

      22 Salton, G., "A Vector Space Model for Automatic Indexing" 18 (18): 613-620, 1975

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-03-25 학회명변경 영문명 : 미등록 -> Korea Intelligent Information Systems Society KCI등재
      2015-03-17 학술지명변경 외국어명 : 미등록 -> Journal of Intelligence and Information Systems KCI등재
      2015-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-02-11 학술지명변경 한글명 : 한국지능정보시스템학회 논문지 -> 지능정보연구 KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2004-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2003-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2001-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.51 1.51 1.99
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      1.78 1.54 2.674 0.38
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