• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.9-14
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• 2018

This study aims to investigate an importance of Author keyword with analysis the position of author keyword. An analysis was carried out on the position of author keyword. we examined an importance of Author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In the next stage, we performed analysis on correlation between network centrality measures and the position of keyword. As a result, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality both has a high value in 4th author keyword order. eigenvector centrality was the comparatively effective method to separate of author keyword order method than other 3 centrality. Correlation analysis result shows that the network analysis value are increasing in order. This study has significance in that it was able to examine the author keyword behavior. Future research is needed to identify and supplement future situational factors, behavior, and psychology.

• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.121-142
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• 2014

This study aims to investigate the importance of author keyword with analysis the position of author keyword in journal. In the first stage, an analysis was carried out on the position of author keyword. We examined the importance of author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality and effective size of structural hole. In the next stage, We performed analysis on correlation between network centrality measures and the position of author keyword. The result of correlation analysis on network centrality measures and the position of author keyword shows that there are the more significant areas of the result of the correlation analysis on degree centrality, betweenness centrality and the position of keyword. In addition, These results show that we need to consider that the possible way as measuring the importance of author keyword in journal is not only a term frequency but also degree centrality and betweenness centrality.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.143-150
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• 2019

This study aimed to analyze the keyword related to Health Disparity in Korea through the method of keyword network analysis and to establish a basic database for suggesting ideas for prospective studies in physical education. To achieve the goal, this study crawled co-occured keyword with 'health' and 'disparity' from news casted in 20 different channels. The duration of the news was 3 months, from September 11th, 2018 to December 11th. The results are as follows. First, among the news during recent 3 months, there were 1,383 keyword related to health disparity and this study selected 173 keyword which had co-occured over 3 times. Second, the inclusiveness of the network was 97.674% and the density was .038. Third, analyzing news related to health disparity, 'mortality' was the most co-occured keyword and 'disparity', 'reinforcement', 'the most', 'health', '6 times', 'Seoul', 'half', 'medicine', and 'local' were shown similarly. And common keyword in 4 centrality were 13 keyword. Lastly, by analyzing eigenvector centrality, significantly different result has shown. 'Disparity' was the most co-occured keyword. Based on this result, this study showed the necessity for reinforcing the public physical education in public education system in Korea. In order to achieve it, the field of physical education must look beyond present elite-focused physical education to public physical activity.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.48-51
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• 2018

The traditional documents analysis was centered on words based system was implemented using a morpheme analyzer. These traditional systems can classify used words in the document but, cannot help to user's document understanding or analysis. In this problem solved, System needs extract for most valuable paragraphs what can help to user understanding documents. In this paper, we propose system extracts paragraphs of normalized XML document. User insert to system what filename when wants for analyze XML document. Then, system is search for keyword of the document. And system shows results searched keyword. When user choice and inserts keyword for user wants then, extracting for paragraph including keyword. After extracting paragraph, system operating maintenance paragraph sequence and check duplication. If exist duplication then, system deletes paragraph of duplication. And system informs result to user what counting each keyword frequency and weight to user, sorted paragraphs.

• Health Policy and Management
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• v.29 no.2
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• pp.138-145
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• 2019

Background: The purpose of this study is supposed to figure out the keyword network from 2009 to 2018 with social network analysis and provide the research data that can help the Korea government's policy making on smoking cessation. Methods: First, frequency analysis on the keyword was performed. After, in this study, I applied three classic centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) with R 3.5.1. Moreover, I visualized the results as the word cloud and keyword network. Results: As a result of network analysis, 'smoking' and 'smoking cessation' were key words with high frequency, high degree centrality, and betweenness centrality. As a result of looking at trends in keyword, many study had been done on the keyword 'secondhand smoke' and 'adolescent' from 2009 to 2013, and 'cigarette graphic warning' and 'electronic cigarette' from 2014 to 2018. Conclusion: This study contributes to understand trends on smoking cessation study and seek further study with the keyword network analysis.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.179-187
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• 2020

In a social environment where population aging is rapidly progressing, the healthcare service market is growing fast with the increasing interest in health and quality of life based on rising income levels and the evolution of technology. In this study, after keywords were extracted from Korean and US patent data published on KIPRIS from 2000 to October 2019, frequency analysis, time series analysis, and keyword network analysis were performed. Through this, the change of technology trends were identified, which keywords related to healthcare was shifted from traditional medical words to ICT words. In addition, although the keywords in Korean patents are 55% similar to those in the US, they show an absolute gap in patent production volume. In the next study, we will analyze various data such as domestic and international research and can obtain meaningful implications in the global market on the identified keywords.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1467-1475
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• 2013

In this paper, we investigate a mutual associative relation between keyword when we used keyword in the papers. We propose how to organize mutual association between the key keyword and sub keyword in the micro battery and energy harvesting.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.91-98
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• 2019

The present study was performed to investigate the general research trends of dyslexia and learning disability to explore the centrality of related variables though analysis of keyword networks. Data were collected from ten years articles research information sharing service(RISS) which is provided by korea education and research information service(KERIS). The research subjects selected for the analysis were keyword cleansing work, extraction major keyword using KrKwic program and using NodeXL program to Visualize the center of connection between keyword. The results of this were as follows. First, totally 72 of keyword were extracted from keyword cleansing process and among those keyword. major keywords included learning disability, dyslexia, RTI. Second, analysis of the betweenness centrality of dyslexia and learing disabilities shows that learning disabilities are a key word that has been addressed in the study of dyslexia and learning disabilities in korea. The results of these studies suggest a method of analyzing trends in qualitative and qualitative analysis in relation to dyslexia and learning disorder.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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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.