• 대한한방내과학회지
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• 제42권4호
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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.

• 대한간호학회지
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• 제49권5호
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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.

• 산업경영시스템학회지
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• 제42권3호
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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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• 제15권10호
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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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• 제15권4호
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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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권3호
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• pp.79-84
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• 2021

고차원의 데이터를 처리하기 위해서는 데이터의 성질을 유지하면서 특징을 잘 반영할 수 있는 특징 추출 방법이 필요하다. 주성분분석 방법은 고차원 데이터에 포함된 정보를 저차원의 데이터로 변환하여 원래 데이터의 변수 수보다 적은 수의 변수로 고차원 데이터를 표현 할 수 있는 방법으로서 데이터의 특징 추출을 위한 대표적인 방법이다. 본 연구에서는 데이터가 고차원인 경우 데이터 특징 추출을 위한 주성분 분석에 있어서 주성분 변수 선정 시 적응적 상관도를 기반으로 한 주성분 분석 방법을 제안한다. 제안하는 방법은 입력 데이터간의 상관 관계를 기반으로 상관도를 적응적으로 반영하여 데이터의 주성분을 분석함으로써 다른 여러 변수에 중복적으로 상관도가 높은 변수와 주성분을 유도하는데 연관성이 적은 변수를 주성분 변수 후보 대상에서 제외시키고자 한다. 고유벡터 계수 값에 의한 주성분 위계를 분석하고 위계가 낮은 주성분이 변수로 선정이 되는 것을 막고 또한 상관 분석을 통하여 데이터의 중복 발생이 데이터 편향을 유도하는 것을 최소화하 하고자 한다. 이를 통하여 주성분 변수 선정 시 데이터 편향성의 영향을 줄임으로써 실제 데이터의 특징을 잘 나타내는 주성분 변수를 선정하는 방법을 제안하고자 한다.

• 한방재활의학과학회지
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• 제32권3호
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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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• 제24권8호
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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.

• 한국융합학회논문지
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• 제13권2호
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• pp.1-12
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• 2022

본 연구는 장애 유아에 대한 최근 10년간의 사회적 인식을 알아보기 위해 인터넷 기반의 빅데이터 분석 시스템인 Textom을 활용하였다. Textom으로 수집된 자료는 데이터 클리닝 과정을 거쳐 빈도가 높은 순으로 50개의 키워드가 선정되었으며, 의미연결망 분석을 위해 UCINET6으로 중심성 분석과 CONCOR분석을 실시하였다. 분석된 자료는 NetDraw를 활용하여 시각화하였다. 그 결과 '교육, 요구, 부모, 통합교육' 등의 키워드가 빈도수, 연결 및 위세 중심성에서 높은 순위를 차지하였다. 그리고 매개 중심성은 '부모, 교사, 문제, 프로그램, 상담'이 높은 순위를 차지하였다. CONCOR분석에서는 '장애, 유아, 진단, 프로그램'의 키워드를 중심으로 하는 4개 군집이 형성되었다. 이러한 연구 결과를 바탕으로 장애 유아에 대한 사회적 인식의 주제가 무엇인지 살펴보고, 주제별 시사점을 논하였다.

• 한국문헌정보학회지
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• 제56권4호
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• pp.205-222
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• 2022

본 연구는 전문지식을 갖지 않은 건설기술자와 건설사업 참여자가 건설 실무에서 중요도가 높은 단어와 단어 간의 상호 연관관계를 쉽게 이해할 수 있도록 정보서비스를 개선하고자 하였다. 이를 위해 텍스트마이닝과 네트워크 중심성을 이용하여 건설기술정보시스템에서 가장 많이 사용하고 있는 기술정보, 사례정보 및 원가절감 등 건설실무정보에 대해 단어의 출현 빈도, 주제 모형화, 네트워크 중심성을 분석하였다. 이러한 분석을 통해 도로, 포장, 교량, 터널 등 도로공사와 관련한 설계, 시공, 사업관리, 시방·기준, 유지관리 등이 건설 실무에서 중요한 정보로 파악되었다. 또한, 연결 중심성과 고유벡터 중심성 측정을 통해 중요도가 높은 단어 간의 상관도를 분석하였다. 상관도 분석을 통해 기술정보를 확충한다면 보다 유용한 정보를 제공할 수 있다는 결과를 얻었다. 끝으로, 연구 결과가 갖는 제약과 이에 따른 추가적인 연구를 제시하였다.