• Fashion & Textile Research Journal
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• v.21 no.3
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• pp.277-287
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• 2019

This study analyzes the online issues of design innovation by a fashion creative director. The study selected fashion house Gucci as the main subject and analyzed social media posts. As for study methods, a social matrix program Textom 2.0 collected 13,014 nouns and adjectives using 'Gucci Collection' as a search keyword from Naver Blogs from March to August 2014 and from March to August 2016. Design related issues were derived through semantic network analysis using Ucinet6 and the NetDraw program. The results of the keyword frequency analysis showed that social media user interest for the Gucci collection increased based on the rapid increase in the number of posts from 1,064 to 2,126 after changing the fashion creative director. The results of visualization of semantic network analysis and content analysis also showed that the main issues related to the Gucci collection design changed after the replacement of the fashion creative director. The study found that issues formed around the product information worn by celebrities for promotion purposes during the 2014 period; however, during the 2016 period, issues were formed around 'vintage' and 'retro' runway concepts with design styles related to Alessandro Michele, the new creative director.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• The Korean Journal of Archival Studies
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• no.43
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• pp.147-177
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• 2015

This study presents the research trends of Records and Archives Management in Korea by analyzing the articles of the Records and Archives Management in Korea. For the study, 479 articles from 5 academic journals published between 2004 and 2013 were analyzed. The study employed content analysis and network analysis. As a result, summary of the study is as follows: First, the most frequently used keywords in the area of Korean Archival Studies were 'Record and Archive Management' and 'Archivist'. However 'Electronic Records'. 'Archival Reference Service' and 'Appraisal' have been used the most frequently when these general words have been excluded. Second, most participating institutions in journals, during the given period of the study, were Myongji University, Hankuk University of Foreign Studies, Chung-Ang University, and Pusan National University. Especially, MyongJi University and Chung-Ang University are core institutions in the Korean Archival Studies community.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3571-3587
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• 2021

For accurately analysing and forecasting the social networks of China's political, economic and social power elites, it is necessary to develop a database that collates their information. The development of such a database involves three stages: data definition, data collection and data quality maintenance. The present study recommends distinctive solutions in overcoming the challenges that occur in existing comparable databases. We used organizational and event factors to identify the Chinese power elites to be included in the database, and used their memberships, social relations and interactions in combination with flows data collection methodologies to determine the associations between them. The system can be used to determine the optimal relationship path (i.e., the shortest path) to reach a target elite and to identify of the most important power elite in a social network (e.g., degree, closeness and eigenvector centrality) or a community (e.g., a clique or a cluster). We have used three social network analysis tools (i.e., R, UCINET and NetMiner) in order to find the important nodes in the network. We compared the results of centrality rankings of each tool. We found that all three tools are providing slightly different results of centrality. This is because different tools use different algorithms and even within the same tool there are various libraries which provide the same functionality (i.e., ggraph, igraph and sna in R that provide the different function to calculate centrality). As there are chances that the results may not be the same (i.e. centrality rankings indicating the most important nodes can be varied), we recommend a comparison test using different tools to get accurate results.

• Health Policy and Management
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• v.31 no.3
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• pp.335-343
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• 2021

Background: The purpose of this study was to extract the major areas of interest in health insurance research in Korea, and infer policy agendas related to health insurance by analyzing research keywords. Methods: For this study, 2,590 articles were selected from among 7,459 academic papers related to health insurance published between January 1987 and December 2018, which were looked up using the Research Information Sharing Service (RISS). Keyword extraction and keyword network analysis were performed using the KrKwic, KrTitle, and UCINET software. Results: First, the number of studies in the area of health insurance continued to increase in all government terms, and it was not until after the 2000s that the subjects of health insurance researches were diversified. Second, degree centrality showed that 'medical expenditure' and 'medical utilization' were consistently high-ranking keywords regardless of the government in power. Aging and long-term care insurance-related keywords were ranked higher in the Lee Myung-bak government, Park Geun-hye government, and Moon Jae-in government. Third, betweenness centrality showed the same high ranking in key topics such as medical expenditure and medical utilization, while the ranking of key keywords differed depending on the interests and characteristics of each government policy. Conclusion: We confirm that health insurance as a research topic has been the main theme in Korean health care research fields. Research keywords extracted from articles also corresponded to the main health policies promoted during each government period. Efforts to systematically investigate policy megatrends are needed to plan adaptive future policies.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.3
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• pp.1-15
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• 2022

The purpose of this study is to compare and analyze the perception of men's fashion before and after the COVID-19 pandemic. TEXTOM allowed the collection of Big Data based on the term 'men's fashion'. As for the data collection periods, Jan. 1, 2018 to Dec. 31, 2019 was set as the pre-COVID-19 era, while Jan. 1, 2020 to Dec. 31, 2021 was set as the post-COVID-19 era. The top 50 words in terms of appearance frequency were extracted from the data. The extracted words were processed using network centrality analysis and CONCOR analysis using Ucinet 6. Research findings were as follows. 1) In the pre-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'men's fashion', 'brand', 'daily look', 'suit', and 'department store'. These words came up with a high TF-IDF values. Network centrality analysis discovered that 'men', 'fashion', 'men's fashion', 'brand', and 'suit' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and styles', 'fashion show', 'purchase', and 'collection'. 2) In the post-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'brand', 'men's fashion', 'discount', 'women', and 'luxury'. These words also displayed high TF-IDF values. Network centrality analysis found that 'fashion', 'men', 'brand', 'men's fashion', and 'discount' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and style', 'fashion show', 'purchase', and 'situation'. 3) Before the outbreak of the pandemic, men were interested in suits to wear to the office, daily look, and fashion shows in Milan and Paris. They often purchased menswear in multi-brand and open stores. However, they were more interested in sneakers, casual styles, and online fashion shows as social distancing and working from home became common. Most purchased menswear through online platforms.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.211-218
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• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• Journal of Fashion Business
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• v.27 no.3
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• pp.132-149
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• 2023

This study aims to analyze Instagram hashtags based on big data to investigate changes in consumer trends and perceptions of vegan fashion, and to derive strategies for revitalizing vegan fashion brands based on derived results. Among social media, Instagram was selected as a collection channel, and Instagram hashtags for 'Vegan Fashion' were collected from July 1, 2021 to December 31, 2021. After conducting semantic network analysis with the Ucinet 6 program based on the collected data, the CONCOR analysis on vegan fashion showed the following four clusters: 'Veganism practiced with fashion', 'Bag type of vegan fashion brand', 'Sharing vegan fashion', and 'Diversification of eco-friendly products'. Analysis results showed that the Instagram hashtag for vegan fashion confirmed the MZ generation's increased interest in vegan fashion and their thoughts to recommend and share frequently used items or brand products to people around them. CONCOR analysis of vegan fashion brands showed the following four groups: 'Differentiating the material of vegan bags', 'Eco-friendly products of vegan fashion brands', 'Interest in vegan shoes', and 'Donation campaign of vegan fashion brands'. CONCOR analysis on Meaningout showed the following four clusters: 'MZ Generation's Meaningout Start-up', 'Recommendation Platform for Skin Products', 'Value Consumption Trend for Eco-friendly Clothing', and 'Interest in Eco-friendly Packaging'. The results of this study on vegan fashion, a practical eco-friendly movement that can require changes in social responsibility and perception as issues that directly affect animals, the environment, and humans, are expected to provide basic data to help domestic vegan fashion brands develop marketing strategies.