• 제목/요약/키워드: Keyword Co-occurrence Network

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다중빈도 키워드 가시화에 관한 연구 (A Study on Multi-frequency Keyword Visualization based on Co-occurrence)

  • 이현창;신성윤
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2018년도 춘계학술대회
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    • pp.103-104
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    • 2018
  • Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

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다중빈도 키워드 가시화에 관한 연구 (A Study on Multi-frequency Keyword Visualization based on Co-occurrence)

  • 이현창;신성윤
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2018년도 춘계학술대회
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    • pp.424-425
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    • 2018
  • Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

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출현회수에 따른 키워드 가시화 연구 (Keyword Visualization based on the number of occurrences)

  • 이현창;신성윤
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2019년도 춘계학술대회
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    • pp.484-485
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    • 2019
  • Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

  • PDF

키워드 빈도수에 따른 시각화 연구 (Keyword Visualization based on the Number of Occurrences)

  • 이현창;신성윤
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2021년도 추계학술대회
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    • pp.565-566
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    • 2021
  • Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

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다학제 분야 학술지의 주제어 동시발생 네트워크를 활용한 기술예측 연구 (A Study on Technology Forecasting based on Co-occurrence Network of Keyword in Multidisciplinary Journals)

  • 김현욱;안상진;정우성
    • 한국경영과학회지
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    • 제40권4호
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    • pp.49-63
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    • 2015
  • Keyword indexed in multidisciplinary journals show trends about science and technology innovation. Nature and Science were selected as multidisciplinary journals for our analysis. In order to reduce the effect of plurality of keyword, stemming algorithm were implemented. After this process, we fitted growth curve of keyword (stem) following bass model, which is a well-known model in diffusion process. Bass model is useful for expressing growth pattern by assuming innovative and imitative activities in innovation spreading. In addition, we construct keyword co-occurrence network and calculate network measures such as centrality indices and local clustering coefficient. Based on network metrics and yearly frequency of keyword, time series analysis was conducted for obtaining statistical causality between these measures. For some cases, local clustering coefficient seems to Granger-cause yearly frequency of keyword. We expect that local clustering coefficient could be a supportive indicator of emerging science and technology.

동시 출현 기반 키워드 네트워크 기법을 이용한 이동식 사다리 추락 재해 위험 요인 연관 구조 모델링 (Correlational Structure Modelling for Fall Accident Risk Factors of Portable Ladders Using Co-occurrence Keyword Networks)

  • 황종문;신성우
    • 한국안전학회지
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    • 제36권3호
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    • pp.50-59
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    • 2021
  • The main purpose of accident analysis is to identify the causal factors and the mechanisms of those factors leading to the accident. However, current accident analysis techniques focus only on finding the factors related to the accident without providing more insightful results, such as structures or mechanisms. For this reason, preventive actions for safety management are concentrated on the elimination of causal factors rather than blocking the connection or chain of accident processes. This greatly reduces the effectiveness of safety management in practice. In the present study, a technique to model the correlational structure of accident risk factors is proposed by using the co-occurrence keyword network analysis technique. To investigate the effectiveness of the proposed technique, a case study involving a portable ladder fall accident is conducted. The results indicate that the proposed technique can construct the correlational structure model of the risk factors of a portable ladder fall accident. This proves the effectiveness of the proposed technique in modeling the correlational structure of accident risk factors.

국내 통합의학 저널의 연구 동향에 대한 계량서지학적 분석 : Integrative Medicine Research를 중심으로 (A Bibliometric Analysis of Research Trends in Domestic Integrative Medicine Journals : Focused on Integrative Medicine Research)

  • 김대진;윤태형;이종록;최병희
    • 대한통합의학회지
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    • 제12권2호
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    • pp.197-210
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    • 2024
  • Purpose : This study aimed to analyze research trends in the field of integrative medicine through a bibliometric analysis of articles published in Integrative Medicine Research (IMR) journal from 2017 to 2022. Methods : Articles published in IMR journal between 2017 and 2022 were searched using the Web of Science database on August 22, 2023. The analysis was performed using the Bibliometrix and Biblioshiny tools in R (version 4.3.1) and VOSviewer (version 1.6.19). Results : The key findings were as follows: average citations per article (9.41), total authors (1,142), single-authored articles (12), average articles per author (0.27), average co-authors per article (5.27), and rate of international co-authorships (15.69 %). The most-cited article was on the cryopreservation of cells or tissues and their clinical applications. The top keyword analysis by author keywords showed that "acupuncture" was the most frequently used keyword (33 times). Co-occurrence network analysis showed 85 high-frequency keywords that appeared five or more times, and the top five keywords by total link strength were "acupuncture," "herbal medicine," "prevalence," "alternative medicine," and "complementary." The study found that, contrary to the trend in complementary and alternative medicine research in Korea, the IMR journal actively conducts intervention studies to provide clinical evidence. Conclusion : In the IMR journal, "acupuncture" was the most frequent of author keywords. The analysis of keyword trend topics over time showed that the keyword "systematic review" continued to appear from 2020 to 2022, and the keyword "clinical practice guideline" appeared for the first time in 2021. In particular, the co-occurrence network analysis highlighted keywords related to intervention research, in contrast to domestic research trends. While this study analyzed only one journal, future studies expanding the category of integrative medicine and increasing the number of journals analyzed may provide further insights.

연구 논문 네트워크 분석을 이용한 수소 연구 동향 (Exploration of Hydrogen Research Trends through Social Network Analysis)

  • 김혜경;최일영
    • 한국수소및신에너지학회논문집
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    • 제33권4호
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    • pp.318-329
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    • 2022
  • This study analyzed keyword networks and Author's Affiliation networks of hydrogen-related papers published in Korea Citation Index (KCI) journals from 2016 to 2020. The study investigated co-occurrence patterns of institutions over time to examine collaboration trends of hydrogen scholars. The study also conducted frequency analysis of keyword networks to identify key topics and visualized keyword networks to explore topic trends. The result showed Collaborative research between institutions has not yet been extensively expanded. However, collaboration trends were much more pronounced with local universities. Keyword network analysis exhibited continuing diversification of topics in hydrogen research of Korea. In addition centrality analysis found hydrogen research mostly deals with multi-disciplinary and complex aspects like hydrogen production, transportation, and public policy.

Analysis of Laughter Therapy Trend Using Text Network Analysis and Topic Modeling

  • LEE, Do-Young
    • 웰빙융합연구
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    • 제5권4호
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    • pp.33-37
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    • 2022
  • Purpose: This study aims to understand the trend and central concept of domestic researches on laughter therapy. For the analysis, this study used total 72 theses verified by inputting the keyword 'laughter therapy' from 2007 to 2021. Research design, data and methodology: This study performed the development and analysis of keyword co-occurrence network, analyzed the types of researches through topic modeling, and verified the visualized word cloud and sociogram. The keyword data that was cleaned through preprocessing, was analyzed in the method of centrality analysis and topic modeling through the 1-mode matrix conversion process by using the NetMiner (version 4.4) Program. Results: The keywords that most appeared for last 14 years were laughter therapy, depression, the elderly, and stress. The five topics analyzed in thesis data from 2007 to 2021 were therapy, cognitive behavior, quality of life, stress, and the elderly. Conclusions: This study understood the flow and trend of research topics of domestic laughter therapy for last 14 years, and there should be continuous researches on laughter therapy, which reflects the flow of time in the future.

주제어 네트워크 분석(network analysis)을 통한 국내 감정노동의 연구동향 탐색 (Exploration of Emotional Labor Research Trends in Korea through Keyword Network Analysis)

  • 이남연;김준환;문형진
    • 융합정보논문지
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    • 제9권3호
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    • pp.68-74
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    • 2019
  • 본 연구는 최근 10년 동안(2009-2018) 국내 학술지에 발표된 감정노동(emotional labor) 관련 892편의 논문을 텍스트 마이닝(text-mining) 및 네트워크 분석(network analysis)을 활용하여 연구동향을 파악하는 것이 목적이다. 이를 위해 이들 논문의 주제어를 수집 및 코딩하여 최종적으로 871개의 노드(node)와 2625개의 링크(link)로 변환시켜 네트워크 텍스트로 분석하였다. 첫째, 네트워크 텍스트 분석 결과로 동시출현빈도에 따른 상위 4개 주요 주제어는 번아웃, 이직의도, 직무스트레스, 직무만족 순으로 나타났으며, 연결중심성에 따른 상위 4개 주제어들의 빈도와 연결중심성 모두 비교적 높은 것으로 확인되었다. 둘째, 연결중심성 상위 4개의 주제어를 바탕으로 자아(ego)연결망 분석을 실시하여 각 네트워크의 연결중심도에 대한 주제어를 제시하였다.