• Journal of Digital Convergence
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• v.20 no.5
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• pp.53-59
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• 2022

The purpose of this study is to examine the factors related to learning through analysis of domestic research trends in literacy and to present the direction of literacy education. Research papers from 1993 to February 2022 were collected using RISS. 'Literacy' and 'Education' were used as search keywords, and 200 papers were selected for analysis. As a result of analysis using keyword network analysis, 118 keywords appeared at least three times out of a total of 810 keywords. The order of the keywords with the highest frequency is 'digital literacy', 'media literacy', and 'elementary school'. The following direction was suggested through the analysis results. First, it is required to establish an online teaching and learning resource platform and link it with education policy. Second, it is necessary to set literacy competencies and seek ways to improve competencies. Third, a digital-based convergence education model should be developed. This study is meaningful in that it analyzed the most recent literacy studies and suggested the direction of literacy education.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.235-243
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• 2022

This paper aims to find promising export items for market expansion of defense export items. Germany, the UK, and France were selected as export target countries to obtain unstructured forecast data on weapons system acquisition plans for the next ten years by each country. Using the TF-IDF in text mining analysis, keywords that appeared frequently in data from three countries were derived. As a result of this paper, keywords for each country's major acquisition projects drawing. However, most of the derived keywords were related to mainstay weapon systems produced by domestic defense companies in each country. To discover promising export items from text mining, we proposed that the drawn keywords are distinguished as similar weapon systems. In addition, we assort the weapon systems that the three countries will get a plan to acquire commonly. As a result of this paper, it can be seen that the current promising export item is a weapon system related to the information system. Prioritizing overseas demands using key words can set clear market entry goals. In the case of domestic companies based on needs, it is possible to establish a specific entry strategy. Relevant organizations also can provide customized marketing support.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.1
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• pp.17-33
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• 2023

Disasters in coastal regions are a constant source of damage due to their uncertainty and complexity, leading to the proposal of green infrastructure as a nature-based solution that incorporates the concept of resilience to address the limitations of traditional grey infrastructure. This study analyzed trends in research related to coastal disasters and green infrastructure by conducting a co-occurrence keyword analysis of 2,183 articles collected from the Web of Science (WoS). The analysis resulted in the classification of the literature into four clusters. Cluster 1 is related to coastal disasters and tsunamis, as well as predictive simulation techniques, and includes keywords such as surge, wave, tide, and modeling. Cluster 2 focuses on the social system damage caused by coastal disasters and theoretical concepts, with keywords such as population, community, and green infrastructure elements like habitat, wetland, salt marsh, coral reef, and mangrove. Cluster 3 deals with coastal disaster-related sea level rise and international issues, and includes keywords such as sea level rise (or change), floodplain, and DEM. Finally, cluster 4 covers coastal erosion and vulnerability, and GIS, with the theme of 'coastal vulnerability and spatial technique'. Keywords related to green infrastructure in cluster 2 have been continuously appearing since 2016, but their focus has been on the function and effect of each element. Based on this analysis, implications for planning and management processes using green infrastructure in response to coastal disasters have been derived. This study can serve as a valuable resource for future research and policy in responding to and managing various disasters in coastal regions.

• Human Ecology Research
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• v.60 no.4
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.351-379
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• 2023

The goal of this study is to investigate the domestic and international research trends on metaverse related researches. To achieve this goal, a set of 913 journal articles were collected from KCI (Korea Citation Index), 232 articles from WoS (Web of Science), and 277 articles from WoS-CPCI (Conference Proceeding Citation Index). A descriptive analysis shows the number of researches has been increased radically, and the mostly researched subject areas are interdisciplinary, computer science, and education in KCI, business and economics in WoS, and computer science in WoS-CPCI. The co-occurrence network analysis using author keywords revealed that technology related terms such as virtual reality and augmented reality showed high centrality measures in all of the databases, and the cluster analysis resulted in education and metaverse platform related keywords cluster from KCI, bibliometric analysis related keywords cluster from WoS, and all the metaverse technology related keywords cluster from WoS-CPCI.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.47-54
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• 2023

In this paper, we propose the technique of optimal search keyword extraction and retrieval for news article classification. The proposed technique was verified as an example of identifying trends related to North Korean construction. A representative Korean media platform, BigKinds, was used to select sample articles and extract keywords. The extracted keywords were vectorized using word embedding and based on this, the similarity between the extracted keywords was examined through cosine similarity. In addition, words with a similarity of 0.5 or higher were clustered based on the top 10 frequencies. Each cluster was formed as 'OR' between keywords inside the cluster and 'AND' between clusters according to the search form of the BigKinds. As a result of the in-depth analysis, it was confirmed that meaningful articles appropriate for the original purpose were extracted. This paper is significant in that it is possible to classify news articles suitable for the user's specific purpose without modifying the existing classification system and search form.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.77-89
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• 2022

Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.5
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• pp.409-419
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• 2016

In general, Area studies have very flexible field of research, so it is very difficult to proceed all field of research at the same time. Due to this, researches on Area studies have been changed the field of research and research trends according to age. So it is important to identify research trends for performing Area studies. Also, interests for understanding the research trend of Area studies are increasing constantly. In this paper, we analyze research trends of Mediterranean Area studies in Korea by using co-appearance keywords. To do this, we first analyze article types and extract co-appearance keywords on articles of 『Journal of Mediterranean Area Studies』, which is the representative journal of Mediterranean region in Korea. In details, trends analysis of Mediterranean Area studies would be performed by using cp-keywords of article and visualizing network graph forms.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.55-68
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• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

• The Research Journal of the Costume Culture
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• v.31 no.1
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• pp.34-52
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• 2023

In this study, text mining was conducted on the product names of skirts, pants, shirts/blouses, and dresses to analyze the characteristics of keywords appearing in online shopping product names. As a result of frequency analysis, the number of keywords that appeared 0.5% or more for each item was around 30, and the number of keywords that appeared 0.1% or more was around 150. The cumulative distribution rate of 150 terms was around 80%. Accordingly, information on 150 key terms was analyzed, from which item, clothing composition, and material information were the found to be the most important types of information (ranking in the top five of all items). In addition, fit and style information for skirts and pants and length information for skirts and dresses were also considered important information. Keywords representing clothing composition information were: banding, high waist, and split for skirts and pants; and V-neck, tie, long sleeves, and puff for shirts/blouses and dresses. It was possible to identify the current design characteristics preferred by consumers from this information. However, there were also problems with terminology that hindered the connection between sellers and consumers. The most common problems were the use of various terms with the same meaning and irregular use of Korean and English terms. However, as a result of using co-appearance frequency analysis, it can be interpreted that there is little intention for product exposure, so it is recommended to avoid it.