• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.278-291
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• 2016

This study examined research trends via network analyses of keywords appeared in academic research articles about news reports in South Korea during the last 10 years from 2006 to 2015. Keyword network analyses of 4410 keywords from 1108 articles suggested that framing, agenda setting, third-person effect, selective exposure, and uses and gratification were main theories but most studies used framing theory. Research areas included news reports on politics, economics, science, world issues, or tour. However, research on news reports covering culture, sports or daily life were not identified. In terms of media, research on both traditional and emerging media were ample. Research on broadcasting new, online news, and social media were frequently observed.

• 재활복지
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• v.21 no.1
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• pp.1-26
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• 2017

The main purpose of this paper is identifying the trends of researches in the realm of social welfare for disabled people. It tries to introduce an alternative analytic approach - key word network analysis - that might surpass the shortage of analysis methods of existing studies which have mainly relied on descriptive analysis. For this purpose the authors constructed a database composed of key words and informations about research methods of the data sources of this study. The sources are such researches as doctoral theses and the papers of Korean Journal of Social Welfare and Journal of Rehabilitation Research which were published during the 20 years from 1996 to 2015. Total numbers of the thesis or papers analysed at this study are 1,034. The results are shown in three ways as follows: First, the method trends of selected researches. Second the lists of interested types and population groups of the disabled persons and interested issues. Third, the intellectual structure of the research. Based on the findings of this study, some suggestions are given considering desirable directions of future researches in relation to perspectives, methods, targets and subjects of them.

• Journal of the Korean Regional Science Association
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• v.32 no.3
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• pp.29-43
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• 2016

The purpose of this study is to derive research subject that has been overlooked in previous studies and contribute to seek to the direction of research in rural development by analyzing the studies in the last 30 years on rural society. In this study, Social Network Analysis was used for identifying the changes in research themes and connection structure of keyword. The study shows that in the previous Roh Moo-Hyun's Administration from 1986 to 2000, the convergence of the research is not active. In terms of the connection structure of keyword, lots of keywords are connected to the 'Migration, IMF, Satisfaction, Green Tourism' but its form is not complicated. In the Roh Moo-Hyun's Administration from 2001 to 2007, the academic exchanges and convergence of keywords on rural development were promoted research. The connection structure of keyword was formed like a complex cluster associated with 'The Rural Elderly, Rural Tourism, Rural Development Policy, Urban-Rural Comparison'. Although some scholars who study 'Women's Studies, Tourism' formed the cluster, its form is still passive. Since 2008 until now, the keyword network of rural development research clustered densely and formed singly. It reveals that the convergence of research subjects has proceeded actively. And studies such as the 'Community, Participation, Social capital, Quality of life, Social networks, Alternative food movement' have begun.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.68-74
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• 2019

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.465-476
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• 2020

This study used social network analysis to analyze trends and the knowledge structure of research in international trade. To this end, 4,840 keywords were extracted and analyzed from 1,797 papers contained in the Journal of Int'l Trade and Industry Studies, the Korea Trade Review, and the Journal of Korea Trade from 2003 to 2019. The results reveal that the distribution of keywords in the trade studies, as with other intellectual networks, followed a power-law distribution. Some differences were observed in the top 20 keywords across journals, with total factor productivity, economic growth, and Korea-US FTA ranking high only in the Journal of Int'l Trade and Industry Studies. Global value chain and trust emerged as a topic that attracted new researchers' attention in the 2011-2019 period. Interest in E-Trade, WTO, and internationalization has declined in recent years. The conventional international trade research trend analyses have predominantly featured qualitative analysis by descriptive method in general, but this study is meaningful in that it employs quantitative analysis using social network analysis techniques.

• Annual Conference on Human and Language Technology
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• 2011.10a
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• pp.79-82
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• 2011

본 논문은 다양한 주제 분야의 연구 성과물을 제공하는 학술DB에서 주제어(Keyword) 정보를 바탕으로 SNA(Social Network Analysis)기법을 적용해 검색어와 연관도가 높은 연관검색어를 제공하는 것을 그 목적으로 한다. 이를 위해 주제어들 간의 가중치(Weight)를 계산한 뒤 Ego Network 분석을 통해 검색어와 연관된 연관주제어를 추출하고 이를 기존 학술DB에서 제공한 연관검색어와 비교 정리하였다. 그리고 정리된 결과를 연관규칙 마이닝기법, 유사계수를 적용해 연관도측면에서 비교 평가하였다.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Journal of IKEEE
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• v.20 no.3
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• pp.235-240
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• 2016

In this study, we use the data collected from Twitter, as an SNS(Social Networking Service), for service innovation. This data was collected and processed by Flume. The data set in May 2016 was 4,766 and 15,543 from company S and company X, respectively. We were able to figure out the emotional atmosphere of the two companies through the sentiment analysis(SA) and to find out about the vertical relationship through the bibliometric analysis(BA). Furthermore, we were able to grasp the horizontal relationship through the social network analysis(SNA). It was concluded that SNS was worth while to derive an innovative item.

• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.39-51
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• 2023

Objective : This study seeks to unveil the intellectual framework of research surrounding children's occupations by utilizing social network analysis of keywords from studies focused on childhood. Methods : From August 2003 to August 2023, we analyzed 3,364 keywords extracted from 270 research articles in the Korean Citation Index with the keyword "Child and Occupation" using the NetMiner program. Results : Research on children's work has increased quantitatively over the past decade. Keywords exhibiting a high degree of centrality in the realm of child occupation research included Task (0.055), Group therapy (0.040), Working memory (0.037), Intervention (0.033), Performance (0.030), Language (0.026), Ability (0.026), Skill (0.024), and Program (0.023). Notably, the weighted terms in the Word Network included Evaluation-Tool (30), School-Student (15), and Activity-Participation (15). The primary keywords from each topic in topic modeling were Activity (0.295), Disability (0.604), Education (0.356), Skill (0.478), School (0.317), Function (0.462), Disorder (0.324), Language (0.310), Comprehension (0.412), and Training (0.511). Conclusion : This study describes the trends in the domestic field of pediatric occupational research. These efforts provided valuable insights into pediatric occupational therapy in South Korea.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.244-247
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• 2018

본 연구는 우리나라 중등교사 임용시험의 형태가 개편된 2014년도 이후부터 2018년도 현재까지 진행된 중등교사 임용시험 관련 선행연구들을 언어네트워크 분석 방법론을 활용하여, 그 연구동향을 분석하였다. 본 연구에서는 2014년도 이후 5년간 진행된 중등교사 임용시험 관련 연구 55건을 대상으로 주요 핵심어 추출 및 워드클라우드 제시, 주요 핵심어의 언어네트워크 전체 분석 및 3종 중심성(연결정도, 근접, 매개) 분석, 네트워크 값을 반영한 네트워크 그림 시각화 작업 등을 진행하였다. 중등교사 임용시험 관련 선행연구의 주요 핵심어는 분석, 문항, 출제, 인식, 임용후보자, 교과교육학, 국어과, 선정경쟁시험, 개선, 예비교사, 교과내용학, 기출문항, 임용교사, 제도, 탐색 등이었다. 이들 상위 빈도 핵심어들은 나름 높은 연결정도를 가지고 다른 핵심어들간의 의미연결망을 구축하고 있음을 확인하였다, 이런 연구결과는 중등교사 임용시험 주제 연구 진행을 할 때, 연구주제 선정 및 방향 설정에 도움을 줄 것으로 기대된다.