• Transactions of the Korean hydrogen and new energy society
• /
• v.33 no.2
• /
• pp.130-141
• /
• 2022

Due to continuous climate change, greenhouse gases in the atmosphere are gradually accumulating, and various extreme weather events occurring all over the world are a serious threat to human sustainability. Countries around the world are making efforts to convert energy sources from traditional fossil fuels to renewable energy. Hydrogen energy is a clean energy source that exists infinitely on Earth, and can be used in most areas that require energy, such as power generation, transportation, commerce, and household sectors. A fuel cell, a device that produces electric and thermal energy by using hydrogen energy, is a key field to respond to climate change, and major countries around the world are spurring the development of core fuel cell technology. In this paper, research trends in China, the United States, Germany, Japan, and Korea, which have the highest number of papers related to fuel cells, are analyzed through keyword network analysis.

• Journal of Korean Library and Information Science Society
• /
• v.47 no.3
• /
• pp.95-114
• /
• 2016

The purpose of this preliminary study is to collect specific examples of book reports and understand semantic characteristics of them through semantic network. The analysis was conducted with 23 book reports which classified by three groups. The keywords were selected from the of book reports. Five types of keyword network were composed based on co-occurrence relations with keywords. The result of this study is following these. First, each keyword network of book reports of groups and individuals is shown to have different structural characteristics. Second, each network has different high centrality keywords according to the result analysis of 3 types of centrality(degree centrality, closeness centrality, betweenness centrality). These characteristic means that keyword network analysis is useful in recognizing the characteristics of not only groups' and but also individual's book reports.

• Journal of the Korea Convergence Society
• /
• v.8 no.5
• /
• pp.169-178
• /
• 2017

In various research fields, it is important to identify the trends and meaningful patterns in large volumes of text data. We examined the research trends and patterns in global journal articles related to aviation and airlines from 1997 to 2016 using keyword network analysis. Keyword network models were constructed, and centrality (degree and betweenness) analysis was performed using 25,959 articles from the Scopus database. The results suggested that the recent research trends in aviation and airlines could be quantitatively described through keyword network analysis. The engineering and social science fields were the most relevant fields with keywords related to aviation and airlines. In addition, it was shown that betweenness centrality increased with the degree centrality of keywords. The results of this study could be applied to establish policies and suggest further research topics in the field of aviation and airlines based on empirical data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.721-732
• /
• 2021

This study analyzed keywords for agricultural R&D using the textmining method to examine the trend of agricultural R&D. Data used for the analysis included R&D project information provided by NTIS, and the research and development step by year from 2003 to 2018 were classified and applied. The TF-IDF approach was used as the analysis method, and ranking was derived based on score. Furthermore, we analyzed by grouping for similar keywords. The main analysis results are as follows. First, agricultural R&D trends are changing according to the introduction of new technologies and changes in the external environment. Second, keyword changes appeared with a time lag in the R&D step. The main keywords are changing in the order of basic research - applied research - development research. Third, the main keyword of agricultural R&D was 'rice.' However, the direction and purpose of the research were changing according to changes in the domestic and foreign agricultural environments.

• Journal of Korean Academy of Nursing
• /
• v.51 no.4
• /
• pp.442-453
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1273-1284
• /
• 2016

In this paper, we analyzed the patents on machine learning using keyword network analysis and clique analysis. To construct a keyword network, important keywords were extracted based on the TF-IDF weight and their association, and network structure analysis and clique analysis was performed. Density and clustering coefficient of the patent keyword network are low, which shows that patent keywords on machine learning are weakly connected with each other. It is because the important patents on machine learning are mainly registered in the application system of machine learning rather thant machine learning techniques. Also, our results of clique analysis showed that the keywords found by cliques in 2005 patents are the subjects such as newsmaker verification, product forecasting, virus detection, biomarkers, and workflow management, while those in 2015 patents contain the subjects such as digital imaging, payment card, calling system, mammogram system, price prediction, etc. The clique analysis can be used not only for identifying specialized subjects, but also for search keywords in patent search systems.

• Journal of East-West Nursing Research
• /
• v.22 no.1
• /
• pp.17-23
• /
• 2016

Purpose: The purpose of this study was to conduct a keyword analysis for exploring the symptoms of Mibyeong and related factors of Korean nurses from domestic nursing research journals from 2000 to 2015. Methods: A total of 63 studies were chosen for analysis using the keywords of "nurses", "fatigue", "pain", "sleep", "digestion", "depression", "anger", "anxiety", "stress", and "quality of life." Results: Fifteen out of 63 studies were published in the Journal of Korean Academy of Nursing Administration and studies were increasing rapidly since 2007. Keyword analysis revealed that majority of the studies were about stress, fatigue, and sleep disturbance. Symptoms of complaints in nurses were similar to those of Mibyeong in Korean Medicine. This study found that there was a need to utilize a feasible interventions in order to manage health in individuals. It is important to mange symptoms of Mibyeong in nurses since they are more vulnerable to it. Conclusion: The concept of Chi-Mibyeong may be helpful for nurses to promote their health as a prevention in Korean medicine before the onset of illness.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.1
• /
• pp.41-53
• /
• 2020

Supply chain management (SCM) became a critical success factor for firms. As a result, researchers have carried out related research on SCM. This study aims to explore the research trends in SCM in Korea using network text analysis. We collected the information of 586 articles published in Korean journals using the RISS database, and analyzed the network generated by keywords proposed in the articles. The results showed that there are five research keyword clusters such as logistics, information systems, partnership, risk management, and sustainability.

• Journal of Fisheries and Marine Sciences Education
• /
• v.26 no.1
• /
• pp.156-165
• /
• 2014

The purpose of this study was to find research trends of smart learning. For this, we identified the research's characteristics such as the subject or keyword of research, method, data collection, and statistical analysis method. The 2,865 articles published from 1995 to 2013 were gathered from five Korean academic journals related to smart learning. Among them, research keyword, areas, research method, data collection method, and statistical analysis method were analyzed on 596 papers. The findings of this study were as follows: (a) Smart learning papers such keyword likes u-learning, m-learning, and smart-learning were emerging after 2006. Smart learning papers with ICT related topics were highly increased after 2000, but they were decreased after 2006. Smart learning papers with e-learning related keywords were steadily increased after 2000 through 2013. (b) The research field of deign had the highest portion in smart learning research, but managing had the lowest portion. (c) Development was mainly used as a research method. Both questionnaire and experiment were mainly used for collecting data methods. T-test and frequency analysis were mainly used as statistical analysis methods.

• Journal of Korea Trade
• /
• v.24 no.5
• /
• pp.71-86
• /
• 2020

Purpose - This study investigates research trends by searching for English keywords and abstracts in 1,511 Korean journal articles in the Korea Citation Index from the 2002-2019 period using the term "Port." The study aims to lay the foundation for a more balanced development of port research. Design/methodology - Using abstract and keyword data, we perform frequency analysis and word embedding (Word2vec). A t-SNE plot shows the main keywords extracted using the TextRank algorithm. To analyze which words were used in what context in our two nine-year subperiods (2002-2010 and 2010-2019), we use Scattertext and scaled F-scores. Findings - First, during the 18-year study period, port research has developed through the convergence of diverse academic fields, covering 102 subject areas and 219 journals. Second, our frequency analysis of 4,431 keywords in 1,511 papers shows that the words "Port" (60 times), "Port Competitiveness" (33 times), and "Port Authority" (29 times), among others, are attractive to most researchers. Third, a word embedding analysis identifies the words highly correlated with the top eight keywords and visually shows four different subject clusters in a t-SNE plot. Fourth, we use Scattertext to compare words used in the two research sub-periods. Originality/value - This study is the first to apply abstract and keyword analysis and various text mining techniques to Korean journal articles in port research and thus has important implications. Further in-depth studies should collect a greater variety of textual data and analyze and compare port studies from different countries.