• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.559-570
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• 2017

우리나라는 과거부터 현재까지 미래 성장동력 육성을 목표로 정부주도하에 국가 R&D 투자를 점진적으로 늘려왔다. 그 결과, 최근에는 GDP 대비 연구개발비 비중이 세계 최고 수준에 이르렀다. 이렇게 연구개발 예산의 양적인 확대와 함께 연구개발 예산의 효율적 활용은 더욱 중요한 과학기술 분야의 정책적 이슈로 부각되고 있다. 연구개발 예산의 효율적인 집행을 위해서는 R&D 사업의 유사 중복성의 검토가 필수적이지만, 대부분의 유사 중복성 검토는 전문가의 직관적인 판단에 근거하여 이루어져왔다. 하지만, 전문가의 직관에만 의지한 판단은 때로는 불명확하거나 잘못된 결과를 가져올 수도 있다. 따라서, 본 연구에서는 네트워크 분석을 통해 정부 R&D 사업의 유사 중복성을 체계적으로 검토하기 위한 데이터기반의 방법론을 제안하여 전문가의 직관에 의한 유사 중복성 검토를 보완할 수 있는 가능성을 모색하고자 한다. 먼저, 본 연구에서는 정부 R&D사업 유사영역의 전체적인 구조 및 형태와 국가과학기술연구회 소속 25개 정부출연연구기관 R&D사업의 유사영역의 전반적인 형태를 시각화하여 유사영역을 파악하고 직관적인 판단과 선택을 할 수 있는 의사결정 정보를 제공하는데 초점을 두었다. 이를 위해, NTIS의 2015년 데이터를 사용하여 과제 키워드 기반으로 동시단어출현 분석을 수행하였다. 본 분석을 통해 25개 기관의 세부적인 유사연구영역 형태를 제시하였으며, 국내의 과학기술정책적 또는 과학기술학적인 현상들을 시각화하였다. 그 결과, 국내 출연연 R&D사업이 기관별 고유영역이 확고히 보이는 Mode 1적인 형태와 사회경제적인 맥락과 필요 및 유망성을 따르고, 다학제적, 적용중심적이며 과제별로 다양한 과제수행기관들이 과제들을 동시에 수행하는 Mode 2적인 형태가 출연연의 R&D사업 내에 공존하고 있음을 확인하였다.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.153-171
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• 2017

Many readers tend to read books of a specific author and to expand their reading areas according to the author. This study chose Edgar Allan Poe and analyzed the image of the author using co-recommended authors and books by other readers. The frequencies of co-occurred authors and books were investigated and the relations of authors and books were analyzed with network analysis methods. As a result, genre images of Poe, related authors, and related books are discovered. This study also suggested the methods to identify the image of a author, related author groups, and related books for libraries' reading programs and book curation.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.63-83
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• 2016

As the usage of information resources produced in various media and forms has been increased, the importance of metadata as a tool of information organization to describe the information resources becomes increasingly crucial. The purposes of this study are to analyze and to demonstrate the intellectual structure in the field of metadata through co-word analysis. The data set was collected from the journals which were registered in the Core collection of Web of Science citation database during the period from January 1, 1998 to July 8, 2016. Among them, the bibliographic data from 727 journals was collected using Topic category search with the query word 'metadata'. From 727 journal articles, 410 journals with author keywords were selected and after data preprocessing, 1,137 author keywords were extracted. Finally, a total of 37 final keywords which had more than 6 frequency were selected for analysis. In order to demonstrate the intellectual structure of metadata field, network analysis was conducted. As a result, 2 domains and 9 clusters were derived, and intellectual relations among keywords from metadata field were visualized, and proposed keywords with high global centrality and local centrality. Six clusters from cluster analysis were shown in the map of multidimensional scaling, and the knowledge structure was proposed based on the correlations among each keywords. The results of this study are expected to help to understand the intellectual structure of metadata field through visualization and to guide directions in new approaches of metadata related studies.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.509-521
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• 2019

The purpose of this study is to examine the patterns of the keywords in journals in the field of Cognitive Behavioral Therapy (CBT) to identify the knowledge structure of CBT studies in Korea. To compare CBT studies from Korea and abroad, 234 articles (2008-2019) published on "Cognitive Behavior Therapy in Korea" and 2,316 articles (1977-2019) published on "Cognitive Therapy and Research" were collected. The data were analyzed using NetMiner 4.3. The co-word analysis was done by calculating the cosine similarity matrix of major keywords, followed by visualizing the network. The results of this study identified the main interests of Korean CBT scholars, and categorized the knowledge structure of CBT in Korea into 9 research areas: "scale validation"; "perfectionism and entrapment"; "cognitive, emotional, and relationship characteristics of schizophrenic patients"; "cognitive characteristics and treatment of borderline personality disorder and depression/bipolar disorder patients"; "adaptation and psychological health"; "cognitive characteristics and treatment of patients with social anxiety disorder"; "causes and co-morbidities of depression"; "acceptance and commitment therapy"; and "understanding and the treatment of binge eating disorder patients." This study is meaningful in that it has reviewed the accumulated knowledge in the CBT field in Korea for the past 11 years, and suggests future tasks for development to improve the standards of CBT practice.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Korean Journal of Ornithology
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• v.25 no.2
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• pp.126-132
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• 2018

For balanced development of ornithological research in Korea, it is important to review what birds and what research topics have been studied so far. We quantitatively investigated the trends of domestic ornithological research using text-mining and co-word analysis. As a result of studying 372 articles published in the Korean Journal of Ornithology, which is the most representative ornithological journals, words related to research topics such as population and community monitoring, first record of species and breeding ecology, and heavy metal pollution in birds have been widely used in research articles. Except for subjects such as monitoring and first record of species, studies have not been conducted widely. It was also found that research were concentrated on specific birds such as Anas platyrhynchos, Calidris alpina, and Anas poecilorhyncha. The present study, which analyzed the research topics and avian taxa that were relatively active until now and those which were insufficient, suggests what we should do in the future for the balanced development of ornithological research in Korea.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.65-86
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• 2021

In the foreign language education, learners are an important part of education, however in the Korean language education, the study of learners was insufficient compared to the contents of education, teaching methods and textbooks. Therefore, it is meaningful to analyze how learner research, especially learning strategy research, has been conducted and derive areas that need research for better education. In this study, co-word analysis was conducted on the titles of academic journals and dissertations in order to analyze the learning strategy research in Korean language education. I found it is about "reading" that the most studies related to Korean language learners' learning strategies were conducted and those studies' subjects mostly were 'Chinese international students' and 'marriage-immigrants'. In addition, the results of the subgroup analysis on the research topic show four major subgroups: a group related to 'reading for academic purposes', a group related to 'request, rejection, conversation, etc.', a group related to 'writing', and a group related to 'vocabulary, listening'. This shows that the researchers' major interests in studying Korean learner's strategies are "reading" and "speaking" and their studies have been concentrated in the specific areas. Therefore, it is necessary for researchers to study various functions and subjects in Korean language learner's learning strategies.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of Information Management
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• v.41 no.2
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• pp.1-30
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• 2010

This study analyzed research output in dominant research areas of 19 national research institutions. Policy knowledge produced by the institutions during the past 5 years mainly concerned 10 policies dealing with economy and society issues. Similarities between the research subjects of the institutions were displayed by MDS mapping. The study also identified issue attention cycles of the 5 chosen policies and examined the correlation between the issue attention cycles and the yields of policy knowledge. The knowledge structure of each policy was mapped using co-word analysis and Ward's clustering. It was also found that the institutions performing research on similar subjects demonstrated citation preferences for each other.

• 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.