• Journal of the Korean earth science society
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• v.34 no.2
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• pp.148-161
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• 2013

The purpose of the study was to investigate the relationship between the communication status in group and the semantic network of science gifted students. Seven small groups, 5 members in each, participated in small group activities, in which they discussed the calculation of earth density. Both the communication status in group and the semantic network of science gifted students were analyzed using KrKwic, Ucinet 6.0 for Windows. As a result, the semantic network of prime movers in group represented more frequently used words, lesser rate of component, and higher density than that of out lookers. It means that the prime movers have coherent knowledge compared to out lookers, and they output more knowledge for problem solving than out lookers. Therefore, the results of this study may be applied to evaluating the cognitive level of science gifted students and group organization for small group activity.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.45-50
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• 2020

The purpose of this study is to analyze relationships among essential keywords in curriculum. The number of 1,935 keyword was collected from 644 manuscripts published between 2002 and 2019. For data analysis, this study selected softwares of KrKwic and KrTitle to compose a 1-mode network matrix and UCINET 6 and NetDraw to implement network analysis and visualization. Results are as follows. First, the frequency of keyword was curriculum, curriculum development, national curriculum, competency-based curriculum, 2015 revised national curriculum, curriculum implementation, understanding by design, competency, teacher education, school curriculum, and IBDP from highest to lowest. Second, degree centrality was curriculum development, curriculum, competency-based curriculum, national curriculum, 2015 revised national curriculum, understanding by design, competency, key competency, high school curriculum, textbook, curriculum implementation, teacher education, and IBDP from highest to lowest.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.479-487
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• 2017

The purpose of this study was to identify the most influential soccer players by appling social network analysis. The subjects were the German national soccer team and the Korean national soccer team participated in the 2016 Brazil World Cup. The pass collected data provided by FIFA were analyzed by social network analysis using the Ucinet6 program and pass success rate. The results are as follows. First, the soccer player with a lot of passes had a high connection centrality in pass-through networks and high proximity. Second, the German national soccer team has appeared key players as Phillip Lahm and Kroos player, and a key player of the Korean national soccer team was Ki,S.Y. Third, the German national soccer team's quantitative indicator value of proximity center and pass success rate appeared higher than the Korean national soccer team's.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.62-71
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• 2009

This paper is a study on the elected korean leading actors & directors who have stable mobility power for audience and social networking. We elected 153 most reliable Korea actors and directors of stardom" (50 directors, 103 actors); they have to put on the name in the 20 high ranks, and more than twice from the total amount of Korean audience about showing 700 movies by 1998-2007. And then we analyze out 'who might be centre in social networking and social networking between actor and director," through 'Centrality Analysis' using the "UCINET" social networking analysis program Finally, we know 'Kim sang-jin director' is 'key men' and 'star' who reduced uncertainty definitely: For a long time, he combines most stable audience power with broad network successfully.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.1-6
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• 2019

This study visualized users reaction about movies based on keywords with high frequency. For this work, we collected data of movie reviews on . A total of six movies were selected, and we conducted the work of data gathering and preprocessing. Semantic network analysis was used to understand the relationship among keywords. Also, NetDraw, packaged with UCINET, was used for data visualization. In this study, we identified the differences in characteristics of review contents regarding each movie. The implication of this study is that we visualized movie reviews made by sentence as keywords and explored whether it is possible to construct the interface to check users' reaction at a glance. We suggest that further studies use more diverse movie reviews, and the number of reviews for each movie is used in similar quantities for research.

• Journal of Information Science Theory and Practice
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• v.8 no.4
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• pp.40-54
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• 2020

This proposed work aims to understand the Korean Academic Circle (KAC)'s research trend on the "One Belt One Road" (OBOR) by employing a quantitative analysis of the recent research articles published by the KAC. To do so, this proposed research has used the well-known network analysis software, Ucinet 6, by which the papers on related topics are collected and filtered from Korea Citation Index. To perform the analytical selection, the proposed work has chosen 'keywords' as the core research object and performed analysis from transverse to longitudinal aspects, and from holistic to individual aspects, respectively; and from this, the KAC's research trend on OBOR is derived. The present work has established that the KAC's attention is continuously increasing on OBOR and has sustainability. Centered on the OBOR, Korean researchers have spread their studies in various dimensions ranging from the issues like China's political economy to Sino-Korea economic and trade exchanges, and so on. The KAC has even combined OBOR with Korea's international development initiatives, which can help Korea benefit from active and sustainable cooperation with China. Moreover, the proposed work has found that Korean researchers have also actively expressed their growing attention, highlighted Korea's interest, and showed concern about China hegemony and Sinocentrism in their recent documented research works.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.375-380
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• 2022

The purpose of this study is to find out consumers' perception and meaning of convenience store convenience food by using big data. For this study, NNAVER and Daum analyzed news, intellectuals, blogs, cafes, intellectuals(tips), and web documents, and used 'convenience store convenience food' as keywords for data search. The data analysis period was selected as 3 years from January 1, 2019 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted using TEXTOM, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, convenience store convenience foods were clustered into health, diversity, convenience, and economy according to consumers' selection attributes. It is expected to be the basis for the development of a new convenience menu that pursues convenience and convenience based on consumers' meaning of convenience store convenience foods such as appropriate prices, discount coupons, and events.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.1-12
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• 2022

The purpose of this study is to identify the social perception characteristics of young children with disabilities over the past decade. For this purpose, Textom, an Internet-based big data analysis system was used to collect data related to young children with disabilities posted on social media. 50 keywords were selected in the order of high frequency through the data cleaning process. For semantic network analysis, centrality analysis and CONCOR analysis were performed with UCINET6, and the analyzed data were visualized using NetDraw. As a result, the keywords such as 'education, needs, parents, and inclusion' ranked high in frequency, degree, and eigenvector centrality. In addition, the keywords of 'parent, teacher, problem, program, and counseling' ranked high in betweenness centrality. In CONCOR analysis, four clusters were formed centered on the keywords of 'disabilities, young child, diagnosis, and programs'. Based on these research results, the topics on social perception of young children with disabilities were investigated, and implications for each topic were discussed.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.1-10
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• 2021

This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.

• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.549-559
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• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.