• Journal of Digital Convergence
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• v.17 no.1
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• pp.91-98
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• 2019

The present study was performed to investigate the general research trends of dyslexia and learning disability to explore the centrality of related variables though analysis of keyword networks. Data were collected from ten years articles research information sharing service(RISS) which is provided by korea education and research information service(KERIS). The research subjects selected for the analysis were keyword cleansing work, extraction major keyword using KrKwic program and using NodeXL program to Visualize the center of connection between keyword. The results of this were as follows. First, totally 72 of keyword were extracted from keyword cleansing process and among those keyword. major keywords included learning disability, dyslexia, RTI. Second, analysis of the betweenness centrality of dyslexia and learing disabilities shows that learning disabilities are a key word that has been addressed in the study of dyslexia and learning disabilities in korea. The results of these studies suggest a method of analyzing trends in qualitative and qualitative analysis in relation to dyslexia and learning disorder.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.85-108
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• 2024

This study aimed to analyze the research trends of research data academic papers from 2010 to 2024 to understand the research status of research data over the past 15 years. To achieve this goal, keyword frequency analysis and network centrality analysis were conducted on 14,921 academic articles published in Scopus DB. The keyword network analysis using UCINET, which was divided into the first period (2010-2014), second period (2015-2019), and third period (2020-2024) according to the period of publication of academic journals, revealed the main keywords studied regardless of the period, the keywords that attracted attention by period, and the keywords that decreased in attention over time. It was found that the most active topic of research data-related research in the last 15 years is data sharing, and most of the keywords with high Degree Centrality also have high Betweenness Centrality. The results of this study can be utilized as a basis for suggesting future research directions in the field of research data in Korea.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.107-115
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• 2013

Various networks can be observed in the world. Knowledge networks which are closely related with technology and research are especially important because these networks help us understand how knowledge is produced. Therefore, many studies regarding knowledge networks have been conducted. The assortativity coefficient represents the tendency of connections between nodes having a similar property as figures. The relevant characteristics of the assortativity coefficient help us understand how corresponding technologies have evolved in the keyword-based patent network which is considered to be a knowledge network. The relationships of keywords in a knowledge network where a node is depicted as a keyword show the structure of the technology development process. In this paper, we suggest two hypotheses basedon the previous research indicating that there exist core nodes in the keyword network and we conduct assortativity analysis to verify the hypotheses. First, the patents network based on the keyword represents disassortativity over time. Through our assortativity analysis, it is confirmed that the knowledge network shows disassortativity as the network evolves. Second, as the keyword-based patents network becomes disassortavie, clustering coefficients become lower. As the result of this hypothesis, weconfirm the clustering coefficient also becomes lower as the assortative coefficient of the network gets lower. Another interesting result concerning the second hypothesis is that, when the knowledge network is disassorativie, the tendency of decreasing of the clustering coefficient is much higher than when the network is assortative.

• International Commerce and Information Review
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• v.19 no.1
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• pp.23-42
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• 2017

International Commerce and Information Review has been playing an important role of disseminating the outstanding research results in the fields such as trade information and systems, e-trade, regional studies, e-commerce, service trade, trade laws since 1999. This paper aims to find the research trends and distinguished characteristics in the field of trade information by analyzing research keywords of the research papers published in this journal using a social network analysis method. Research keyword data collected from the homepage of the academic society were cleaned and transformed into the co-occurrence network data, which are suitable for social network analysis. NodeXL Pro was used to analyze and visualize the pre-processed data. Through clustering analysis, the most important subject fields or interests were identified as well as those which worked as intermediaries for interdisciplinary researches.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.311-314
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• 2018

최근 '미투운동'이 활발히 진행되면서 새로운 페미니즘의 물결을 맞이하였다. 이전의 페미니즘 운동과의 차이점은 SNS 를 통해 익명으로 활동하며 전파속도가 굉장히 빠르다는 것이다. 본 연구는 미투운동의 이러한 특성을 고려하여 실제 트위터 데이터에서 주요 키워드를 파악하고, 해당 키워드의 연관성 및 네트워크 분석으로 사회적 맥락을 알아본다.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.63-73
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• 2018

Academia and research institutes mainly use qualitative methods that rely on expert judgments to understand and predict research trends and science and technology trends. Since such a technique has the disadvantage of requiring much time and money, in this study, science and technology trends were predicted using keyword network analysis. To that end, 13,618 AI (Artificial Intelligence) patent abstracts were analyzed using keyword network analysis in three separate lots based on the period of the submission of each abstract: analysis period 1 (January 1, 2002 - December 31, 2006), analysis period 2 (January 1, 2007 - December 31, 2011), and analysis period 3 (January 1, 2012 - December 31, 2016). According to the results of frequency analyses, keywords related to methods in the field of AI application appeared more frequently as time passed from analysis period 1 to analysis period 3. In keyword network analyses, the connectivity between keywords related to methods in the field of AI application and other keywords increased over time. In addition, when the connected keywords that showed increasing or decreasing trends during the entire analysis period were analyzed, it could be seen that the connectivity to methods and management in the field of AI application was strengthened while the connectivity to the field of basic science and technology was weakened. According to analysis of keyword connection centrality, the centrality value of the field of AI application increased over time. According to analysis of keyword mediation centrality during analysis period 3, keywords related to methodologies in the field of AI application showed the highest mediation value. Therefore, it is expected that methods in the field of AI application will play the role of powerful intermediaries in AI hereafter. The technique presented in this paper can be employed in the excavation of tasks related to regional innovation or in fields such as social issue visualization.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.169-178
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• 2017

In various research fields, it is important to identify the trends and meaningful patterns in large volumes of text data. We examined the research trends and patterns in global journal articles related to aviation and airlines from 1997 to 2016 using keyword network analysis. Keyword network models were constructed, and centrality (degree and betweenness) analysis was performed using 25,959 articles from the Scopus database. The results suggested that the recent research trends in aviation and airlines could be quantitatively described through keyword network analysis. The engineering and social science fields were the most relevant fields with keywords related to aviation and airlines. In addition, it was shown that betweenness centrality increased with the degree centrality of keywords. The results of this study could be applied to establish policies and suggest further research topics in the field of aviation and airlines based on empirical data.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.511-518
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• 2012

In this study, the trend in green technology was observed and the domain of the green technology area that will be actively studied in the future was found by establishing knowledge map in green technology area and comparing and analyzing green technology information in Korea and overseas in time series. For the purpose of this study, network analysis was conducted for the keyword of green technology information provided by green technology information portal site (www.gtnet.go.kr) operated by Korea Institute of Science and Technology Information. Network analysis was conducted using keyword, and change of study subject was found by dividing the analysis result into periods. In the result of network analysis on top 100 keywords from total English keyword, it was found that renewable energy related areas such as solar energy and biomass had high centrality. When the main keyword trend by year was studied, centrality of solar cell, nanotechnology, smart grid, and fuel cell were found to increase, showing that research and development in generation and use of renewable energy are actively made.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.330-333
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• 2016

최근 구제역의 발생으로 인해 농 축산업계 및 관련 산업분야에 막대한 피해를 야기함에 따라, 구제역의 발병에 따른 다양한 사회적 파급효과의 분석이 필요하다. 본 논문에서는 온라인 뉴스를 대상으로 텍스트 마이닝 방법들을 사용하여 구제역으로 인한 경제적, 환경적, 그리고 정책적 파급효과를 분석하는 공학적 방법론을 제안한다. 제안하는 시스템은 먼저, 구제역 관련 온라인 뉴스를 수집한 후, 토픽 모델링의 대표적인 방법 중 하나인 LDA(Latent Dirichlet Allocation)를 활용하여 뉴스 기사로부터 키워드들을 추출한다. 둘째, 추출된 키워드들로부터 구제역으로 인한 파급효과의 분석을 위해 동시출현 키워드 네트워크를 구성한다. 셋째, 키워드 네트워크 타임라인을 통해 각 파급효과들의 변화를 분석한다. 마지막으로, 사례분석을 통해 2010년 7월부터 2011년 12월까지 한국에서 발생한 구제역으로 인한 사회적 파급효과의 분석을 수행하였다.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.393-413
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• 2021

Digital humanities, which creates new and innovative knowledge through the combination of digital information technology and humanities research problems, can be seen as a representative multidisciplinary field of study. To investigate the intellectual structure of the digital humanities field, a network analysis of authors and keywords co-word was performed on a total of 441 papers in the last two years (2019, 2020) at the Digital Humanities Conference. As the results of the author and keyword analysis show, we can find out the active activities of Europe, North America, and Japanese and Chinese authors in East Asia. Through the co-author network, 11 dis-connected sub-networks are identified, which can be seen as a result of closed co-authoring activities. Through keyword analysis, 16 sub-subject areas are identified, which are machine learning, pedagogy, metadata, topic modeling, stylometry, cultural heritage, network, digital archive, natural language processing, digital library, twitter, drama, big data, neural network, virtual reality, and ethics. This results imply that a diver variety of digital information technologies are playing a major role in the digital humanities. In addition, keywords with high frequency can be classified into humanities-based keywords, digital information technology-based keywords, and convergence keywords. The dynamics of the growth and development of digital humanities can represented in these combinations of keywords.