• 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.

• 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.

• 한국항공운항학회지
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• 제31권4호
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• pp.19-27
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• 2023

In this study, a total of 2,584 domestic research papers with the keywords "Aviation Safety" and "Aviation Accidents" were subjected to Text Mining analysis. Various text mining techniques, including keyword frequency analysis, word correlation analysis, network analysis, and topic modeling, were applied to examine the research trends in the field of aviation safety. The results revealed a significant increase in research using the keyword "Aviation Safety" since 2015, with over 300 papers published annually. Through keyword frequency analysis, it was observed that "Aircraft" was the most frequently mentioned term, followed by "Drones" and "Unmanned Aircraft." Phi coefficients were calculated for words closely related to "Aircraft," "Aviation," "Drones," and "Safety." Furthermore, topic modeling was employed to identify 12 distinct topics in the field of aviation safety and aviation accidents, allowing for an in-depth exploration of research trends.

• 정보관리학회지
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• 제31권2호
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• pp.121-142
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• 2014

본 연구는 학술논문의 저자키워드 출현순서에 따른 저자키워드의 중요도를 측정해보고자 하는 연구이다. 먼저 출현순서에 따른 저자키워드의 특징을 분석한 후 네트워크 분석 방법의 연결정도중심성, 근접중심성, 매개중심성, 위세중심성, 그리고 네트워크의 구조적공백성의 효과크기와 같은 지수를 사용하여 학술논문의 저자키워드 출현순서에 따른 저자키워드의 중요도를 측정해보았으며 각각의 네트워크 지수와 저자키워드의 출현순서와의 상관관계분석을 수행하였다. 네트워크 분석 지수 중 연결정도중심성 지수, 매개중심성 지수의 경우 각 학문분야별 저자키워드의 출현순서와의 상관관계의 결과에서의 유의한 분야의 수가 비교적 다른 지수에 비해 많았다. 이와 같은 결과를 통해 저자키워드의 중요도를 단지 출현빈도만으로 판단했던 것에서 벗어나 저자키워드의 중요도 측정을 위한 방법으로 연결정도중심성 지수, 매개중심성 지수도 고려해 볼 수 있음을 알 수 있었다.

• 대한영양사협회학술지
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• 제22권4호
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• pp.310-319
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• 2016

Limitation of a questionnaire survey which is widely used is time and money, limited numbers of participants, biased confidence interval and unreliable results. To overcome these, we performed tendency and network analysis of diet using big Data in Koreans. The keyword on diet were collected from the portal site Naver from January 1, 2015 until December 31, 2015 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. The results showed that diet menu appeared most frequently by N-gram analysis, even though exercise had the highest frequency by simple frequency analysis. In addition, keyword network analysis were categorized into four groups: diet group, exercise group, commercial diet program company group and commercial diet food group. The analysis of seasonality showed that subjects' interests in diet had increased steadily since February, 2015, although subjects were most interested indiet in July, these results suggest that the best strategies for weight loss are based on diet menu and starting diet before July. As people are especially sensitive to diet trends, researches are needed about annual analysis of big data.

• 디지털산업정보학회논문지
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• 제20권2호
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• pp.11-25
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• 2024

This study aims to analyze research trends in digital twin focusing on Germany, the US, and Korea. In Elsevier's Scopus, we collected 4,657 papers about digital twin published in from 2019 to 2023. Keyword frequency and centrality analysis were conducted on the abstracts of the collected papers. Through the obtained keyword frequencies, we tried to identify keywords with high frequency of occurrence and through centrality analysis, we tried to identify central research keywords for each country. In each country, 'digital_twin', 'machine_learning', and 'iot' appeared as research keywords with the highest interest. As a result of the centrality analysis, research on digital twin, simulation, cyber physical system, Internet of Things, artificial intelligence, and smart manufacturing was conducted as research with high centrality in each country. The implication for Korea is that research on virtual reality, digital transformation, reinforcement learning, industrial Internet of Things, robotics, and data analysis appears to have been conducted with low centrality, and intensive research in related areas appears to be necessary.

• 산업융합연구
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• 제21권1호
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• pp.187-192
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• 2023

본 연구는 기계학습의 키워드 출현 빈도 분석과 CONCOR(CONvergence of iteration CORrealtion) 기법을 통한 ICT 교육에 대한 흐름을 탐색한다. 2018년부터 현재까지의 등재지 이상의 논문을 'ICT 교육'의 키워드로 구글 스칼라에서 304개 검색하였고, 체계적 문헌 리뷰 절차에 따라 ICT 교육과 관련이 높은 60편의 논문을 선정하면서, 논문의 제목과 요약을 중심으로 키워드를 추출하였다. 단어 빈도 및 지표 데이터는 자연어 처리의 TF-IDF를 통한 빈도 분석, 동시 출현 빈도의 단어를 분석하여 출현 빈도가 높은 49개의 중심어를 추출하였다. 관계의 정도는 단어 간의 연결 구조와 연결 정도 중심성을 분석하여 검증하였고, CONCOR 분석을 통해 유사성을 가진 단어들로 구성된 군집을 도출하였다. 분석 결과 첫째, '교육', '연구', '결과', '활용', '분석'이 주요 키워드로 분석되었다. 둘째, 교육을 키워드로 N-GRAM 네트워크 그래프를 진행한 결과 '교육과정', '활용'이 가장 높은 단어의 관계로 나타났다. 셋째, 교육을 키워드로 군집분석을 한 결과, '교육과정', '프로그래밍', '학생', '향상', '정보'의 5개 군이 형성되었다. 이러한 연구 결과를 바탕으로 ICT 교육 동향의 분석 및 트렌드 파악을 토대로 ICT 교육에 필요한 실질적인 연구를 수행할 수 있을 것이다.

• 디지털산업정보학회논문지
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• 제18권3호
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• pp.9-23
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• 2022

This study aims to examine research trends on the Internet of Things and smart city based on papers from the United States, Japan, and Korea. We collected 7113 papers related to the Internet of Things and smart city published from 2016 to 2021 in Elsevier's Scopus. Keyword frequency and centrality analysis were performed based on the abstracts of the collected papers. We found keywords with high frequency of appearance by calculating keyword frequency and identified central research keywords through the centrality analysis by country. As a result of the analysis, research on security, machine learning, and edge computing related to the Internet of Things and smart city were the most central and highly mediating research conducted in each country. As an implication, studies related to deep learning, cybersecurity, and edge computing in Korea have lower degree centrality and betweenness centrality compared to the United States and Japan. To solve the problem it is necessary to combine these studies with various fields. The future research direction is to analyze research trends on the Internet of Things and smart city in various regions such as Europe and China.

• 수산해양교육연구
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• 제28권3호
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• pp.872-880
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• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.

• 한국IT서비스학회지
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• 제21권3호
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.