• The Journal of Internal Korean Medicine
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• v.42 no.4
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• pp.628-644
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• 2021

Objectives: This study analyzed, through network analysis and data mining analysis, the relationship between herbs used in osteoporosis prescriptions, diversified the analysis of osteoporosis-related prescriptions, and analyzed the combination of herbs used in osteoporosis-related prescriptions. Methods: The prescriptions used in osteoporosis treatment and experiments were established by conducting a full survey of the papers published by the OASIS site. A database for osteoporosis-related prescriptions was established, herbs were extracted, and the frequency of frequent herbs and prescriptions were investigated using Excel (MS offices ver. 2013). Using the freeware R version 4.0.3 (2020-10-10), igraph, and arules package, network analysis was performed in the first second of prescription composition. Results: Among the osteoporosis-related prescriptions, the most studied prescriptions are as follows.: Yukmijihwang-tang (六味地黃湯) and Samul-tang (四物湯). In the osteoporosis prescription network, herbs with connection centrality, proximity centrality, mediation centrality, and eigenvector centrality appeared in the order of Rehmanniae Radix Preparata, Angelicae Gigantis Radix, Poria Sclerotium, Paeoniae Radix, and Glycyrrhizae Radix et Rhizoma. After extracting the herbal combination network, including the corresponding herbs, and clustering it, it can be divided into drugs of the Yukmijihwang-tang (六味地黃湯) series and the Samul-tang (四物湯). Conclusions: This study could assist researchers in diversifyingy formula analysis in future studies. Moreover, the herbal combination used in osteoporosis prescriptions could be used to search for osteoporosis prescriptions in other databases or to create a new prescription.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.538-549
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• 2019

Purpose: This study aimed to explore and compare the knowledge structure of pain management nursing research, between Korea and other countries, applying a text network analysis. Methods: 321 Korean and 6,685 international study abstracts of pain management, published from 2004 to 2017, were collected. Keywords and meaningful morphemes from the abstracts were analyzed and refined, and their co-occurrence matrix was generated. Two networks of 140 and 424 keywords, respectively, of domestic and international studies were analyzed using NetMiner 4.3 software for degree centrality, closeness centrality, betweenness centrality, and eigenvector community analysis. Results: In both Korean and international studies, the most important, core-keywords were "pain," "patient," "pain management," "registered nurses," "care," "cancer," "need," "analgesia," "assessment," and "surgery." While some keywords like "education," "knowledge," and "patient-controlled analgesia" found to be important in Korean studies; "treatment," "hospice palliative care," and "children" were critical keywords in international studies. Three common sub-topic groups found in Korean and international studies were "pain and accompanying symptoms," "target groups of pain management," and "RNs' performance of pain management." It is only in recent years (2016~17), that keywords such as "performance," "attitude," "depression," and "sleep" have become more important in Korean studies than, while keywords such as "assessment," "intervention," "analgesia," and "chronic pain" have become important in international studies. Conclusion: It is suggested that Korean pain-management researchers should expand their concerns to children and adolescents, the elderly, patients with chronic pain, patients in diverse healthcare settings, and patients' use of opioid analgesia. Moreover, researchers need to approach pain-management with a quality of life perspective rather than a mere focus on individual symptoms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.116-130
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• 2019

This study investigates passenger-authored online reviews of airline services using social network analysis to compare the differences in customer perceptions between full service carriers (FSCs) and low cost carriers (LCCs). While deriving words with high frequency and weight matrix based on the text analysis for FSCs and LCCs respectively, we analyze the semantic network (betweenness centrality, eigenvector centrality, degree centrality) to compare the degree of connection between words in online reviews of each airline types using the social network analysis. Then we compare the words with high frequency and the connection degree to gauge their influences in the network. Moreover, we group eight clusters for FSCs and LCCs using the convergence of iterated correlations (CONCOR) analysis. Using the resultant clusters, we match the clusters to dimensions of two types of service quality models ($Gr{\ddot{o}}nroos$, Brady & Cronin (B&C)) to compare the airline service quality and determine which model fits better. From the semantic network analysis, FSCs are mainly related to inflight service words and LCCs are primarily related to the ground service words. The CONCOR analysis reveals that FSCs are mainly related to the dimension of outcome quality in $Gr{\ddot{o}}nroos$ model, but evenly distributed to the dimensions in B&C model. On the other hand, LCCs are primarily related to the dimensions of process quality in both $Gr{\ddot{o}}nroos$ and B&C models. From the CONCOR analysis, we also observe that B&C model fits better than $Gr{\ddot{o}}nroos$ model for the airline service because the former model can capture passenger perceptions more specifically than the latter model can.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1502-1520
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• 2021

This paper attempts to investigate Korean youth Not in Education, Employment and Training (NEET) and how daily activities and community participation may influence their positive emotions and job search desire. First, we conducted a focus group interview with 16 NEETs who participated in the Kakao NEET Company project. The project allowed participants to experience employment by founding a virtual company in which each participant selected a daily activity to perform as part of the company's operations. Second, the interview responses were categorized and assigned emotional values using the card sorting technique and multi-dimensional analysis (MDS). A total of 11 emotional values were derived through this process. Finally, a social network analysis was conducted in order to measure the density of relations among the emotional values. Results suggest that immersion, confidence, belongingness were the three highest values evaluated by participants. Furthermore, network diagrams imply that the stronger participants perceived social support and belongingness with others, the stronger their responsibility grew, further leading them to establish steady goals. In particular, the high eigenvector score for "desire for job" suggests that emotional values are sequentially connected to the immersion-social support-responsibility-goal-job desire. This sequence suggests that digital services that are developed with the aim to enhance social values such as the Kakao NEET Project may engender motivation and confidence in youth NEETs. The overall results suggest that a systematic approach to policymaking should be considered in order to provide fundamental solutions and expand opportunities for social participation and emotional comfort, as social isolation due to low self-esteem has been reported as one of the reasons for NEETs' failure in the labor market.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.3
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• pp.119-129
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• 2022

Objectives This study aimed to evaluate the effects of Integrative treatment of traditional Korean medicine (TKM) on 7 hospitalized patients after hip fracture surgery, and to identify significant herbs and co-prescribed herbs by using network analysis and association rule mining. Methods A retrospective chart review of the 7 hospitalized patients treated for postoperative hip fractures between January and December 2021 was performed. All TKM treatments for the patients were identified and Wilcoxon signed-rank test was performed to compare hip pain and mobility on admission and discharge. We visualized the network of herbal medicines and complications. By using network analysis, we also identified the significant herbs (high centrality of degree, eigenvector, and sub-graph). Co-prescription patterns for the hip fracture patients were further analyzed by association rule mining. Results We found that TKM treatment significantly relieved hip pain and improved mobility. Accompanying symptoms reported by the patients were general weakness, anorexia, dizziness, delirium, edema, sputum, sore throat, cough, rhinorrhea, and chills. Herbs composed of Sagunja-tang and Samul-tang showed high centralities and high associations with other herbs. In addition, Gupan, Nokyong, Yukjongyong, Useul, and Hyunhosaek were identified as important herbs for postoperative hip fracture patients. Conclusions This study provides evidence for clinical TKM use as an effective postoperative treatment for pain relief and improvement of mobility in patients with hip fractures. In addition, herbs that can be considered in the treatment of patients after hip fracture surgery were identified through network analysis and association rule mining.

• Journal of Korea Trade
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• v.24 no.8
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• pp.171-188
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• 2020

Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

• 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 the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.205-222
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• 2022

This study aims to improve the information service so that construction engineers and construction project participants without specialized knowledge can easily understand the important words and the interrelationships between them in construction practice. To this end, using text mining and network centrality, the frequency of occurrence of words, topic modeling, and network centrality in construction practice information such as technical information, case information, and cost reduction, which are most used in the Construction Technology Digital Library, were analyzed. Through this analysis, design, construction, project management, specifications, standards, and maintenance related to road construction such as roads, pavements, bridges, and tunnels were identified as important in construction practice. In addition, correlations were analyzed for words with high importance by measuring Degree Centrality and Eigenvector Centrality. The result was that more useful information could be provided if the technical information was expanded. Finally, we presented the limitations of the study results and additional studies according to the limitations.