• The Research Journal of the Costume Culture
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• v.27 no.3
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• pp.193-205
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• 2019

A trend analysis of research articles in a field of knowledge is significant because it can help in finding out the structural characteristics of the field and the future direction of research through observing change in a time series. We identified the structural characteristics and trends in text data (keywords) gathered from research articles which in itself is an important task in various research areas. The titles and keywords were crawled from research articles published from 2016 to 2018 in the Research Journal of the Costume Culture (RJCC), one of the representative Korean journal in the field of clothing and textile. After we extracted data comprising English titles and keywords from 195 published articles, we transformed it into a 1-mode matrix. We used measures from network analysis (i.e., link, strength, and degree centrality) for evaluating meaningful patterns and trends in the research on clothing and textile. NodeXL was used for visualizing the semantic network. This study observed change in the clothing and textile research trend. In addition to covering the core areas of the field, the subjects of research have been diversifying with every passing year and have evolved onto a developmental direction. The most studied area in articles published by the RJCC was fashion retailing/consumer psychology while aesthetic/historic and fashion industry/policy studies were covered to a more limited extent. We observed that most of the studies reflecting the identity of RJCC share subject keywords to a significant extent.

• 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.

• Herbal Formula Science
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• v.31 no.2
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• pp.81-98
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• 2023

Objectives : This study aimed to analyze the trends in research and development projects related to herbal medicine and natural products in the field of traditional Korean medicine (TKM) over the past 20 years. Methods : Research projects were identified using "Korean medicine" as the subject heading in the National Science and Technology Information Service. The included projects investigated Korean medicine, natural products, or were related to the TKM industry. Data pre-processing and network analysis were performed using Python and Networkx package, and the network was visualized using the ForceAtlas2 visualization algorithm. Results : 1. Over the study period, 4,020 projects were conducted with a research budget of KRW 835.2 billion. Seven institutions performed over 100 projects each, accounting for 2.4% of all participating institutions, and the top 10 institutions accounted for 58.9% of total projects. 2. Obesity was the most frequently mentioned disease-related keyword. Chronic or age-related diseases such as diabetes, osteoporosis, dementia, parkinson's disease, cancer, inflammation, and asthma were also frequent research topics. Clinical research, safety, and standardization were also frequently mentioned. 3. Centrality analysis found that obesity was the only disease-related keyword identified, alongside TKM-related keywords. Standardization, safety, and clinical trials were identified as central keywords. Conclusions : The study found that research projects in TKM have focused on standardizing and ensuring the safety of herbal medicine, as well as on chronic and age-related diseases. Clinical studies aimed at verifying the effectiveness of herbal medicine were also frequent. These findings can guide future research and development in herbal medicine.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.2
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• pp.121-127
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• 2021

Objectives : The characteristics of depressive symptoms in patients with cancer is different from those in control group. However, few research has focused on the association between depressive symptoms in cancer patients. The aim of this study was to compare the network structure of depressive symptoms between patients with cancer and normal control. Methods : This study was based on cohort data from Korea National Health and Nutrition Examination Survey in 2016-2018. The Patient health Quetionnaire-9 (PHQ-9) was used to assess depressive symptoms in 599 patients with cancer and 599 age-sex matched controls. We estimated network structure of depressive symptom using Isingfit model. Results : There was no significant difference of each PHQ-9 item score. There were strong associations between symptoms were concentration problem-psychomotor activity, anhedonia-depressed mood, and depressed mood-suicidal ideation in both groups. Strength centrality of worthlessness was significantly higher in patients with cancer. Conclusions : These results suggest that worthless is associated with other depressive symptoms more tightly in patients with cancer. Worthless can serve as important treatment targets for intervention of depression in patients with cancer.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.537-544
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• 2022

As the penetration of electric vehicles and development of new models, user experience factors are getting more important in designing connectivity systems for car infotainment services. The primary object of this study is to identify commonalities and differences by comparing user experience factors in the Korean and US electric vehicle markets. This study derived connectivity keywords by text mining the vehicle introduction on the market in each country, and performed centrality, cluster analysis and visualization mapping using the semantic network analysis. As a result, the Korean new electric vehicle connectivity service mainly focused on driving functions such as driving, parking assistance, and charging, while US focused on device connection, convenience function control, app use, entertainment viewing. Based on the analysis, this study presented the practical implications in marketing, system design, and HMI design.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.162-171
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• 2020

The purpose of this study is to examine the social perception of disaster safety education for young children based on Textom big data and to explore the direction of young children's disaster safety education. Researchers collected and analyzed online text data using the keywords 'young children+disaster+safety education' from portal websites from 2014 to 2017. The raw data were then subjected to first and second data refinement process. Based on the frequency analysis results, 50 keywords were selected, and the selected keywords were converted into matrix data for network analysis. The results of the study are: first, the most frequently appeared keyword together with young children's disaster safety education was 'education', followed by 'experience', 'kindergarten', 'prevention', and 'school.' Second, keywords with high centrality in the analysis of centrality also were 'education', 'experience', and 'prevention'. In addition, keywords like 'prevention', 'life', and 'evacuation' appear higher in connection-centricity than frequency ranking, which means that the degree of connection between the words is high. These results suggest that young children need education in during early childhood in order to improve their disaster safety skills, and disaster safety education should be accomplished through 'prevention' and 'experience' in early childhood education institutions.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Journal of the Korean Society for information Management
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• v.30 no.4
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• pp.31-59
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• 2013

The purpose of this study was to examine the intellectual structure of domestic LIS in the 1990s and 2000s using author bibliographic coupling analysis (ABCA). First, cluster analysis and multi-dimensional scaling analysis were performed to examine core subject areas and to map authors in two-dimensional space. Second, network analysis was used to visualize intellectual relationships among subject areas and to reveal the top subject areas for global centrality. Third, the 1990s and 2000s intellectual structures was compared to identify the changes of the intellectual structure over the course of time.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.2
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• pp.163-170
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• 2018

The purpose of this study is to describe quantitatively the characteristics and the structure of the Act on the development, management, etc. of Marinas (the Marinas Act) by analysing its provisions using semantic network analysis. The method of semantic network analysis has its advantages in overcoming limitations of the traditional content analysis method, as it is easy for the user to understand the structure and the shape of a network by figuring out the structural network among words. The object of the analysis is the full text of Marinas Act recently revised from Chapters 1 to 4, while partial analysis is carried out respectively for each chapter from Chapters 2 to 4. The structural characteristic of the Marinas Act shows that the act focuses on the development of marinas, as its main goal is interpreted to set up hardwares and to construct facilities rather than to promote the marina industry itself. Even though some clauses for human capital development and business development are included, they are of less importance compared to the development of marina facilities. This study provides some basic information on the structural characteristics of the current act, which can be referred to in subsequent studies. In the future, it also needs to be complemented through comparative analysis with government policy outcomes and performance of diverse analytical approaches.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.66-71
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• 2021

The purpose of this paper is to identify keywords related to museums, gamification, and visitors, and provide basic data that the museum market can be expanded by using gamification. That used to collect data for blogs, news, cafes, intellectuals, academic information by Naver and Daum which is Web documents in Korea, and Google Web, news, Facebook, Baidu, YouTube, and Twitter for analysis. For the data analysis period, a total of one year of data was selected from April 16, 2020 to April 16, 2021, after Corona. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analysed and visualized using the Netdraw function in the UCINET6 program. In addition, We performed CONCOR analysis to derive clusters for similar keywords. As a result, a total of 25,761 cases that analysing the keywords of museum, gamification and visitors were derived. This shows that the museum, gamification, and spectators are related to each other. Furthermore, if a system using gamification is developed for museums, the museum market can be developed.