• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.354-360
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• 2021

People hope to live a healthy and happy life achieving satisfaction by striking a good work-life balance. Therefore, there is a growing interest in well-aging which means living happily to a healthy old age without worry. This study identified important factors related to well-aging by analyzing news articles published in Korea. Using Python-based web crawling, 1,199 articles were collected on the news service of portal site Daum till November 2020, and 374 articles were selected which matched the subject of the study. The frequency analysis results of text mining showed keywords such as 'elderly', 'health', 'skin', 'well-aging', 'product', 'person', 'aging', 'female', 'domestic' and 'retirement' as important keywords. Besides, a social network analysis with 45 important keywords revealed strong connections in the order of 'skin-wrinkle', 'skin-aging' and 'old-health'. The result of the CONCOR analysis showed that 45 main keywords were composed of eight clusters of 'life and happiness', 'disease and death', 'nutrition and exercise', 'healing', 'health', and 'elderly services'.

• Journal of Korean Home Economics Education Association
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• v.36 no.2
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• pp.65-88
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• 2024

This study aims to explore the knowledge structure of the field of home economics education. To achieve this, the knowledge network of the field of home economics education was analyzed using social network analysis on 758 articles published between 2004 and 2023, focusing on those in the Journal of Home Economics Education Research. The main findings of the study are as follows: First, the knowledge network exhibited characteristics of a small-world network. Papers on children, family, and career maturity significantly influenced the knowledge structure. Second, the knowledge structure is centered around the home economics subject and curriculum and is organized into four groups. A temporal analysis revealed that the influence of core keywords such as perception, content, unit, home economics teachers, practice, behavior, and influence has decreased, while the influence of curriculum, textbook, and development has shown a trend of increasing. Third, the sub-knowledge structures were identified as seven categories. The study found that the influence of 'perception and demand for home economics education' is decreasing, whereas the influence of 'home economics curriculum and textbooks' and 'application of home economics teaching and learning process' is increasing. Additionally, 'adolescent self-esteem and family relationships' and 'home economics curriculum and textbooks' were found to be the most influential in the knowledge structure of home economics education. This research is significant as it demonstrates the temporal changes in the core keywords and sub-structures of the knowledge structure within the field, thereby providing a foundation for understanding and expanding the research knowledge structure in the field of home economics education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.534-541
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• 2021

This study aims to establish an efficient future technology development strategy in the information and telecommunications industry by grasping related technology trends and fusion complexity through an analysis based on standard patents. Analyzing 1,983 patents related to the information and telecommunications industry identified the trends in major patent applicants and detailed technologies in the world. In addition, technology trends were investigated through keyword analysis to examine the degree of complexity in information and communications technology, confirming the direction of research in information technology. Electronic component and wireless communications fields have relatively few standard patents, but they are highly convergent with other industrial technologies. Computer information processes and communication and broadcasting technologies are highly related to each other, so they can be used as standard fusion technologies in standard patents. In addition, standardization activities in optical and image/sound devices are found to be high.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.35-45
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• 2018

Research on the establishment of new governance aimed at efficient policy planning and the implementation thereof by the government has been conducted in response to social changes. Nonetheless, governance is comprehensive and encompasses different meanings; it takes various forms in the process of its actual application. Therefore, systematic classification of research on governance and analysis on its research trend are required. Accordingly, three researchers who majored in policy sciences, business informatics, and library and information science, respectively, searched for theses related to governance published since 2016 from Research Information Sharing Service and conducted a social network analysis (SNA) on them. According to their research results, the main research topics were largely classified into collaborative governance and local governance. Keywords throughout the topics included network, participation, conflict, and trust in line with the characteristics of governance. Representative subjects of governance included education, urban regeneration, and the environment. Further, measurement of betweenness centrality showed local governance was a main topic for convergent research. This study will lead to a greater understanding of research on governance and help activate such research. One limitation of this study is that it analyzed only theses with the keywords but not all theses on governance. Follow-up research should analyze all theses on governance and statistically verify them with SNA indexes.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.53-77
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• 2012

This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.133-145
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• 2022

As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.2
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• pp.15-23
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• 2012

Today, rapid changes in technologies and everyday lives due to the Internet make it is difficult to make predictions about the future. Generally, the best way to predict the future has been proposed by experts. Although expert opinions are very important, they are liable to produce incorrect results due to human error, insufficient information regarding future outcomes and a state of connectedness between people, among other reasons. One of the ways to reduce these mistakes is to provide objective information to the experts. There are many studies that focus on the collection of objective material from papers, patents, reports and the Internet, among other sources. This research paper seeks to develop a forecasting method using World Wide Web search results according to the Google search engine and a network analysis, which is generally used to analyze a social network analysis(SNA). In particular, this paper provides a method to analyze a complexity network and to discover important technologies in the construction field. This approach may make it possible to enhance the overall performance of forecasting method and help us understand the complex system.

• Korean Association of Arts Management
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• no.58
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• pp.153-177
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• 2021

This study attempted to analyze language networks based on the theory of art policy in the New Normal era triggered by COVID-19 and domestic and foreign policy trends. For analysis, data containing key words of "Corona" and "Art" were collected from Google News and Web documents from March to September 2020 to extract 227 refined subject words, and the extracted subject words were analyzed as indicators of frequency and centrality of subject words through the Netminor program. In addition, visualization analysis of semantic networks has been attempted for the analysis of relationships between each topic languages. As a result of the semantic network analysis, the most frequent topic was "Corona," and "Culture and Art," "Art," "Performance," "Online" and "Support" were included in the group with the most frequencies. In the centrality analysis, "Corona" was the most popular, followed by "the era," "after," "post," "art," and "cultural arts," with high frequency, "Corona," "art," and "cultural arts" also dominated most centrality. In particular, the top-level key words in the analysis of frequency and centrality of the topic are 'online' and 'support' and 'policy'. This can be seen as indicating that the rapid rise of non-face-to-face and online content and support policies for the artistic communities are needed due to the dailyization of social distance due to COVID-19.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.340-352
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• 2017

In This study, we examined the gap between supply and demand of forest policy by year through big data analysis for macroscopic evaluation of the 5th Basic Forest Plan. We collected unstructured data based on keywords related to the projects mentioned in the news, SNS and so on in the relevant year for the policy demand side; and based on the documents published by the Korea Forest Service for the policy supply side. based on the collected data, we specified the network structure through the social network analysis technique, and identified the gap between supply and demand of the Korea Forest Service's policies by comparing the network of the demand side and that of the supply side. The results of big data analysis indicated that the network of the supply side is less radial than that of the demand side, implying that various keywords other than forest could considerably influence on the network. Also we compared the trends of supply and demand for 33 keywords related to 27 major projects. The results showed that 7 keywords shows increasing demand but decreasing supply: sustainable, forest management, forest biota, forest protection, forest disease and pest, urban forest, and North Korea. Since the supply-demand gap is confirmed for the 7 keywords, it is necessary to strengthen the forest policy regarding the 7 keywords in the 6th Basic Plan.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.129-137
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• 2018

The aim of this study is to analyze the President Directions Item using Semantic Network Analysis. This study has three contributions. First, this study shows the difference of policy directions through the frequency and contents of key words. Second, this study suggest patterns changes of decision-making of the president and bureaucracy through the key words network structure. Third, this study infers the interaction between the president's will and context of institutions.