• Title/Summary/Keyword: 키워드네트워크 분석

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Analysis of the Role of Library and Information Science Related Research Efforts in Korean Human Computer Interaction Subject Field (국내 HCI 분야 문헌정보학 연구의 역할 분석과 방향성 고찰)

  • Lee, Jee Yeon;Kam, Miah;Han, Nam Gi;Song, Hanna
    • Journal of the Korean Society for information Management
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    • v.33 no.2
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    • pp.177-200
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    • 2016
  • The purpose of this study was to analyze how the Library and Information Science (LIS) related research efforts in Korea contributed to the progress of the Human Computer Interaction (HCI) discipline to identify the potential collaboration research partners and topics. The following data was used for the network and frequency analysis: titles, keywords and affiliation information extracted from the Korean journals serving the LIS fields as well as the conference proceedings of the Korean HCI society. Information communication technology, computer engineering, and technology industry had been active in research collaborations but LIS field was comparatively not active. This study was able to determine that LIS researchers carried out important research but they had not much interests in the topics that required interdisciplinary collaboration. However, there were a number of papers, which were not authored by LIS discipline researchers, in the LIS related journals. In addition, there are a few ongoing interdisciplinary research projects which included LIS researchers. This reveals that it is possible for the LIS field could play an important role in the future if the researchers actively participate the collaborative projects.

Topic Modeling and Keyword Network Analysis of News Articles Related to Nurses before and after "the Thanks to You Challenge" during the COVID-19 Pandemic (COVID-19 '덕분에 챌린지' 전후 간호사 관련 뉴스 기사의 토픽 모델링 및 키워드 네트워크 분석)

  • Yun, Eun Kyoung;Kim, Jung Ok;Byun, Hye Min;Lee, Guk Geun
    • Journal of Korean Academy of Nursing
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    • v.51 no.4
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    • pp.442-453
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    • 2021
  • Purpose: This study was conducted to assess public awareness and policy challenges faced by practicing nurses. Methods: After collecting nurse-related news articles published before and after 'the Thanks to You Challenge' campaign (between December 31, 2019, and July 15, 2020), keywords were extracted via preprocessing. A three-step method keyword analysis, latent Dirichlet allocation topic modeling, and keyword network analysis was used to examine the text and the structure of the selected news articles. Results: Top 30 keywords with similar occurrences were collected before and after the campaign. The five dominant topics before the campaign were: pandemic, infection of medical staff, local transmission, medical resources, and return of overseas Koreans. After the campaign, the topics 'infection of medical staff' and 'return of overseas Koreans' disappeared, but 'the Thanks to You Challenge' emerged as a dominant topic. A keyword network analysis revealed that the word of nurse was linked with keywords like thanks and campaign, through the word of sacrifice. These words formed interrelated domains of 'the Thanks to You Challenge' topic. Conclusion: The findings of this study can provide useful information for understanding various issues and social perspectives on COVID-19 nursing. The major themes of news reports lagged behind the real problems faced by nurses in COVID-19 crisis. While the press tends to focus on heroism and whole society, issues and policies mutually beneficial to public and nursing need to be further explored and enhanced by nurses.

Network Analysis of Keywords Related to Korean Nurse: Focusing on YouTube Video Titles (국내 간호사 관련 동영상 키워드의 네트워크 분석: 유튜브 동영상 제목을 중심으로)

  • Lee, Dongkyun;Lee, Youngjin;Lee, Bogyeong;Kim, Sujin;Park, Haejin;Bae, Sun Hyoung
    • Journal of Home Health Care Nursing
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    • v.29 no.3
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    • pp.278-287
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    • 2022
  • Purpose: To analyze Korean nurse-related channels and video titles on YouTube, the world's largest online video sharing and social media platform, to clarify public opinion and image of nurses. We seek utilization strategies and measures through current status analysis. Methods: Data is collected by crawling video information related to Korean nurses, and correlation is analyzed with frequent word analysis and keyword network analysis. Results: Through the YouTube algorithm, 2,273 videos of 'Nurse' were analyzed in order of recent views, relevance, and rating, and 2,912 videos searched for with the keyword 'Nurse + Hospital, COVID-19, Awareness, University, National Examination' were analyzed. Numerous videos were uploaded, and nursing work that was uploaded in the form of a vlog recorded a high number of views. Conclusion: We could see if the YouTube video shows images of nurses. It has been confirmed that various information is being exchanged rather than information just for promotional purposes.

Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing (인공지능과 간호에 관한 언론보도 기사의 키워드 네트워크 분석 및 토픽 모델링)

  • Ha, Ju-Young;Park, Hyo-Jin
    • Journal of Korean Academy of Nursing
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    • v.53 no.1
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    • pp.55-68
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    • 2023
  • Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

Exploring the Research Trend Changes on Convergence Education of Before and After 2011 in Science Education (2011년 전후의 과학교육분야에서의 융합교육 연구동향의 변화 탐색)

  • Song, Youngwook;Paik, Seoung-Hey
    • Journal of The Korean Association For Science Education
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    • v.40 no.5
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    • pp.531-542
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    • 2020
  • The purpose of this study is to explore the research trend changes of convergence education since 2011 compared to the convergence education research that has been steadily continuing in science education. The trend in convergence education were investigated by comparing the number of publications, research subjects, research content, and topic linkages with previous studies, and using the network analysis method to check recent research trends. In the field of science education, the number of papers related to convergence education has been published more than 8.0% steadily, and it has been increasing since 2012, then decreasing again from 2015 and gradually increasing again from 2017. The subjects of study were high in elementary school students, while those in middle school, high school, and university students were low. While the number of in-service teachers increased, the number of pre-service teachers decreased, and the literature and public increased somewhat. In study content, effectiveness studies decreased, while development studies increased, and theoretical and perception studies appeared similar. In thematic linkage, the intra-science linkage was 23.9%, and the extra-science linkage was 76.1% and engineering/technology and art were high in extra-science linkage. In network analysis, elementary, science, STEAM, and program words have a high frequency of appearance and appear together with other words to lead the network. The educational implications of the research trend of convergence education will be more emphasized in the field of science education in the future, and in order to take root in the education field, research on secondary students should be more actively studied. In addition, it is necessary to move away from research on STEAM-centered program development and effects, and to increase research to establish the philosophical basis and theoretical of convergence education.

A Study on Consumer perception changes of online education before and after COVID-19 using text mining (텍스트 마이닝을 활용한 온라인 교육에 대한 소비자 인식 변화 분석: COVID-19 전후를 중심으로)

  • Sohn, Minsung;Im, Meeja;Park, Kyunghwan
    • Journal of Digital Convergence
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    • v.19 no.1
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    • pp.29-43
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    • 2021
  • Coinciding with the advent of COVID-19, online education is on the rise both domestically and globally, and has become an absolutely necessary and irreplaceable form of education. It is a very curious question what the perception of people about the suddenly growing form of education is, and how it has changed. This study investigated changes in consumers' perception of online education using big data. To this end, we divided the time into four stages: before COVID-19 (November to December 2019), after the triggering of COVID-19 (January to February 2020), right after the online classes started (March to April 2020), after experiencing some online education (May to June 2020). Then we conducted text mining, namely, keyword frequency analysis, network analysis, word cloud analysis, and sentiment analysis were performed. The implications derived as a result of the analysis can help education policy makers and educators working in the field to improve online education quality and establish its future directions.

Content Analysis of the 'Housing' Unit in the 2015 Revised Middle School Technology and Home Economics Textbook Using Text Mining (텍스트 마이닝을 이용한 2015 개정 중학교 기술·가정 교과서의 주생활 단원 내용분석)

  • Kim, Do-Yeon
    • Journal of Korean Home Economics Education Association
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    • v.34 no.2
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    • pp.1-19
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    • 2022
  • The purpose of this study is to analyze the keywords of the middle school textbooks based on the 2015 revision of the technology and home economics curriculum to understand the core concepts and contents composition of the 'housing' unit. Using TEXTOM and UCINET programs, the frequencies and centralities of the keywords were analyzed, and CONCOR analysis was performed. The results are as follows. First, the content system of the 'housing' unit is divided into 'life culture' and 'safety' in the 'family life and safety' area. Second, in the 'safety' section, the frequencies of occurrence of the words were high in the order of indoor, occurrence, use, noise, and safety accidents, in the order of frequency of occurrence. It was confirmed that words related to daily life, safety accidents, and prevention were closely connected to each other. In the 'life culture' section, the frequencies of occurrence were high in the order of space, housing, family, and residential space, and the correlations between these keywords were also high. Third, the most influential core keywords were, indoor and occurrence in the 'safety' section, and space, family, and housing, in the 'life culture' section. Fourth, the 'safety' section were divided into two subunits, 'safe living environment' and 'comfortable living environment', and the 'life culture' section were divided into four subunits, 'living space composition', 'space utilization', 'housing value and lifestyle', and 'housing culture'.

Keyword Analysis of Research on Consumption of Children and Adolescents Using Text Mining (텍스트마이닝을 활용한 아동, 청소년 대상 소비관련 연구 키워드 분석)

  • Jin, Hyun-Jeong
    • Journal of Korean Home Economics Education Association
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    • v.33 no.4
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    • pp.1-13
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    • 2021
  • The purpose of this study is to identify trends and potential themes of research on consumption of children and adolescents for 20 years by analyzing keywords. The keywords of 869 studies on consumption of children and adolescents published in journals listed in Korean Citation Index were analyzed using text mining techniques. The most frequent keywords were found in the order of youth, youth consumers, consumer education, conspicuous consumption, consumption behavior, and character. As a result of analyzing the frequency of keywords by dividing into five-year periods, it was confirmed that the frequency of consumer education was significantly higher betwn 2006 and 2010. Research on ethical consumption has been active since 2011, and research has been conducted on various topics instead of without a prominent keyword during the most recent 5-year period. Looking at the keywords based on the TF-IDF, the keywords related to the environment and the Internet were the main keywords between 2001 and 2005. From 2006 to 2010, the TF-IDF values of media use, advertisement education, and Internet items were high. From 2011 to 2015, fair trade, green growth, green consumption, North Korean defector youths, social media, and from 2016 to 2020, text mining, sustainable development education, maker education, and the 2015 revised curriculum appeared as important themes. As a result of topic modeling, eight topics were derived: consumer education, mass media/peer culture, rational consumption, Hallyu/cultural industry, consumer competency, economic education, teaching and learning method, and eco-friendly/ethical consumption. As a result of network analysis, it was found that conspicuous consumption and consumer education are important topics in consumption research of children and adolescents.

Comparing Corporate and Public ESG Perceptions Using Text Mining and ChatGPT Analysis: Based on Sustainability Reports and Social Media (텍스트마이닝과 ChatGPT 분석을 활용한 기업과 대중의 ESG 인식 비교: 지속가능경영보고서와 소셜미디어를 기반으로)

  • Jae-Hoon Choi;Sung-Byung Yang;Sang-Hyeak Yoon
    • Journal of Intelligence and Information Systems
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    • v.29 no.4
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    • pp.347-373
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    • 2023
  • As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

A Study on Analysis of Research Trends and Intellectual Structure in the Overseas Cataloging Research (해외 목록학 연구동향 및 지적구조 분석)

  • Ji Won Lee;Sung Sook Lee
    • Journal of the Korean Society for information Management
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    • v.41 no.1
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    • pp.367-387
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    • 2024
  • This study aims to identify the recent trends and intellectual structure of international research in the field of catalog, which is undergoing a major change due to the enactment of new standards and rules and the anticipated future. For this purpose, we collected 680 articles published in the 14 years since 2010 and analyzed 1,942 author keywords extracted from them after preprocessing. The main findings of the analysis are as follows First, overseas cataloging research has seen notable growth since 2017. Second, the most frequent research topics were: cataloging, metadata, RDA, university libraries, authority control, linked data, FRBR, catalog, LCSH, libraries, andonline cataloging. Third, the research themes were divided into two clusters, one related to the traditional aspects of library cataloging and the other related to the more recently discussed topics of authority control, cooperative cataloging, RDA, and linked data, which were further subdivided into 14 subclusters. Fourth, we looked at the growth index and standard performance index of the 14 keyword clusters and found that all but one cluster showed growth in terms of discipline growth. This study is significant in that it can be used as a basis for predicting the future development of inventories for Korean academia and the field and for related education.