• Journal of the Korean Society of Safety
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• v.36 no.3
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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.

• Human Ecology Research
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• v.59 no.2
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• pp.245-259
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• 2021

The purpose of this study was to analyze big data to identify the sub-dimensions of ethical consumption, as well as the consumption value associated with ethical consumption that changes over time. For this study, data were collected from Naver and Daum using the keyword 'ethical consumption' and frequency and matrix data were extracted through Textom, for the period January 1, 2016, to December 31, 2018. In addition, a two-way mode network analysis was conducted using the UCINET 6.0 program and visualized using the NetDraw function. The results of text mining show increasing keyword frequency year-on-year, indicating that interest in ethical consumption has grown. The sub-dimensions derived for 2014 and 2015 are fair trade, ethical consumption, eco-friendly products, and cooperatives and for 2016 are fair trade, ethical consumption, eco-friendly products and animal welfare. The results of deriving consumption value keywords were classified as emotional value, social value, functional value and conditional value. The influence of functional value was found to be growing over time. Through network analysis, the relationship between the sub-dimensions of ethical consumption and consumption values derived each year from 2014 to 2018 showed a significantly strong correlation between eco-friendly product consumption and emotional value, social value, functional value and conditional value.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.311-314
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• 2018

최근 '미투운동'이 활발히 진행되면서 새로운 페미니즘의 물결을 맞이하였다. 이전의 페미니즘 운동과의 차이점은 SNS 를 통해 익명으로 활동하며 전파속도가 굉장히 빠르다는 것이다. 본 연구는 미투운동의 이러한 특성을 고려하여 실제 트위터 데이터에서 주요 키워드를 파악하고, 해당 키워드의 연관성 및 네트워크 분석으로 사회적 맥락을 알아본다.

• International Commerce and Information Review
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• v.19 no.1
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• pp.23-42
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• 2017

International Commerce and Information Review has been playing an important role of disseminating the outstanding research results in the fields such as trade information and systems, e-trade, regional studies, e-commerce, service trade, trade laws since 1999. This paper aims to find the research trends and distinguished characteristics in the field of trade information by analyzing research keywords of the research papers published in this journal using a social network analysis method. Research keyword data collected from the homepage of the academic society were cleaned and transformed into the co-occurrence network data, which are suitable for social network analysis. NodeXL Pro was used to analyze and visualize the pre-processed data. Through clustering analysis, the most important subject fields or interests were identified as well as those which worked as intermediaries for interdisciplinary researches.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.47-56
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• 2020

The purpose of this study was to identify the research trends and characteristics of existing research related to the AEO system. The methodology of the study was to utilize the Degree Centrality, Closeness Centrality and Betweenness Centrality presented by the Social Network Analysis (SNA). Keyword network analysis results showed that "MRA", "Logistics Security" were derived from the Degree Centrality results, "MRA", "Logistics Security" from the Closeness Centrality results, and, as a result of the Betweenness Centrality, "AEO Utilization Benefits" and "reliability" were derived from the top keyword results. The analysis of differences in centrality by period also confirmed that trends in research have changed based on specific time points. This study has implications for the study in that it presented worldwide research trends through keyword network analysis of the AEO system.

• Health Policy and Management
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• v.18 no.2
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• pp.86-106
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• 2008

This research analyzed. knowledge structure and its effect factor by analysis of co-author and keyword network in Korea's health policy and administration sector. The data was extracted from 339 articles listed in the Korean Journal of Health Policy and Administration, and was transformed into a co-author and keyword matrix. In this matrix the existence of a link was defined by impact factors which were calculated by the weight value of what the role was and the rate of how many authors contributed. We demonstrated that the research achievement was dependent on the author's status and network index. Analysis methods were neighborhood degree, correspondence analysis, multiple regression and the difference of weight distribution by research fields. Co-author networks were developed as closeness centrality as well as degree centrality by a few high productivity researchers. In particular, power law distribution was discovered in impact factor and research productivity. The effect of the author's role was significant in both the impact factor calculated by the participatory rate and the number of listed articles. Especially, this journal shared its major researchers who had a licensed physician with the Journal of Preventive Medicine and Public Health. Therefore, social scientists were likely to be small co-author network differently from natural scientists. It was so called 'two cultures' phenomenon. This study showed how can we verified academic research structure existed in the unit of journal like as citation networks. The co-author networks in the field of health policy and administration had more differentiated and clustered than preventive medicine and epidemiology fields.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.21-29
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• 2020

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.

• Journal of Korean Clinical Nursing Research
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• v.25 no.1
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• pp.80-90
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• 2019

Purpose: The aim of this study was to identify the research trends of articles published in the Journal of Korean Clinical Nursing Research from 2000 to 2017 by a text network analysis using keywords. Methods: This study analyzed 600 articles. The R program was used for text mining that extracted frequency, centrality rank, and keyword network. Results: From 2000 to 2009, keywords with high-frequency were 'nurse', 'pain', 'anxiety', 'knowledge', 'attitude', and so on. 'Pain', 'nurse', and 'knowledge' showed a high centrality. 'Fatigue' showed no high frequency but a high centrality. Keywords such as 'nurse', 'knowledge', and 'pain' also showed high frequency and centrality between 2010 and 2017. 'Hemodialysis' and 'intensive care unit' were added to keywords with high frequency and centrality during the period. Conclusion: The frequency and centrality of keywords such as 'nurse', 'pain', 'knowledge', 'hemodialysis', and 'intensive care unit' reflect the research trends in clinical nursing between 2000 and 2017. Further studies need to expand the keyword networks by connecting the main keywords.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.7
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• pp.813-818
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• 2015

IoT is the network of physical objects or things embedded with electronics, software, sensors and connectivity to enable it to achieve greater value and service by exchanging data with the manufacturer, operator or other connected devices. Until now many researches have been studied for centering keyword for detecting emerging technology of future and scientometric analysis among the papers. This paper propose searching method of emerging technology for the IOT (internet of things) which is recently interesting area via dynamic analysis for keyword mapping.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.177-178
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• 2019

본 연구는 지방교육자치의 가장 핵심인 '교육감' 관련 연구들의 주요 핵심어들과 그들 간의 관계성을 분석하였다. 본 연구에서는 2009년부터 2018년까지(10년간)의 '교육감' 관련 선행연구 총 93건을 키워드 네트워크 분석 방법론을 활용하여, 주요 핵심어 추출 및 워드 클라우드 제시, 주요 핵심어들 간의 관계성(의미망 네트워크) 분석 등을 진행하였다. 최근 10년간 국내 '교육감' 관련 연구들의 주요 핵심어들은 교육감선거, 주민직선제, 선출제도, 개선방안, 비교연구, 교육자치, 문제점, 지방자치, 교육부장관, 교육위원 등 이었다. 주요 핵심어들(상위 출현빈도)은 높은 밀도와 연결정도를 가지고 상호 네트워크를 형성하고 있었다. 본 연구결과는 향후 진행될 '교육감' 관련 후속연구들의 새로운 연구주제 선정 및 다양한 방향 설정에 기초자료로 활용될 수 있을 것이다.