• 비교교육연구
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• 제28권1호
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• pp.1-24
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• 2018

본 연구는 교육개발협력에 관한 글로벌 연구 동향을 살펴보고, 이를 통해 국내 관련 연구에서의 향후 방향과 시사점을 탐색하는 것을 목적으로 한다. 이를 위해 교육개발협력 분야의 국제 학술지인 "International Journal of Educational Development"를 선정하고, 2002년부터 2017년까지 약 15년간 게재된 연구 논문 966편을 대상으로 연구 초록에 제시된 (저자) 키워드를 텍스트 네트워크 분석하여 시기별, 교육영역별로 연구 주제가 어떻게 변화하고 이에 나타나는 특징이 무엇인지를 알아보았다. 이에 대한 주요 연구 결과는 다음과 같다. 첫째, 분석 대상 전체 논문에 나타난 연구 주제어의 출현 빈도를 살펴본 결과, 교육프로그램관리, 학교수업, 지역공공행정, 교육지원서비스, 초등교육 순으로 높았으며, 빈도 순 상위 20개의 핵심주제어에 대한 네트워크 중앙성 분석 결과는 빈도수 결과와 유사한 상관관계를 나타내었다. 그러나 중등교육, 학습, 교육연구, 교육변화, 교육의질 등의 주제어는 출현 빈도에 비해 높은 중앙성 지수를 나타내고 있어 다른 키워드들과 높은 관계성을 가지고 있었다. 둘째, 시기별 핵심 주제어 분석 결과 MDGs 전기 대비 후기와 SDGs 초기에는 새로운 키워드(초등교육, 초중등학교, 학교수업, 교육의 질, 중등교육, 교육계획)가 다양하게 나타났고, 중앙성 지수에서도 높은 수치를 나타내고 있어 새로운 핵심 연구 주제가 되고 있음을 알 수 있다. 셋째, 교육일반, 기초교육, 중등교육, 고등교육으로 분류한 교육영역별 분석 결과에서는 빈도수와 중앙성이 높은 핵심 주제어가 각각 다소 상이하게 나타나고 있어 영역에 따른 연구 키워드가 구분되고 있다는 특징이 부각되었다. 본 연구는 국제 아젠다로서의 교육개발협력 특성을 고려하여 국제적 수준에서 약 15년간 누적된 연구 논문들을 대상으로 객관적 데이터 분석 프로그램을 활용해 연구 주제의 변화 동향을 조망하였다는데 의의가 있으며, 현재 국내에서 실천적 노력과 더불어 교육개발협력에의 학문적 연구 개발이 지속적으로 강화되어야 할 시점임을 고려할 때, 향후 보다 다양한 분야에서의 연구 개발에서 참고할 만한 시사점을 제공할 수 있을 것이다.

• 한국의상디자인학회지
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• 제22권3호
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• 한국문헌정보학회지
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• 제55권2호
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• pp.147-178
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• 2021

본 연구에서는 국내 오픈액세스 분야의 지적구조 분석을 위해 동시출현단어 분석을 시행하였다. KCI와 RISS를 통해 수집한 국내 오픈액세스 관련 연구물 124편의 논문을 분석 대상으로 선정했으며, 제목과 초록에서 총 1,157개의 키워드를 추출하였다. 선정된 키워드를 대상으로 네트워크 분석을 시행하여 3개 영역과 20개 세부 군집으로 구분하여 제시하였다. 패스파인더 네트워크를 통해 키워드들의 지적 관계를 시각화하였으며, 가중 네트워크를 위한 중심성 분석을 통해 핵심 키워드를 확인하였다. 다음으로 군집분석을 실시하여 5개의 군집을 도출하고, 다차원 축적 지도상에 표시함으로써 키워드 간의 상관관계에 따른 지적구조를 제시하였다. 이러한 연구 결과는 국내 오픈액세스 분야의 지적구조를 시각적으로 파악할 수 있게 하며, 앞으로 국내 오픈액세스 연구의 방향성을 예측하는데 기초 자료로 활용할 수 있을 것으로 기대한다.

• Human Ecology Research
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• 제60권1호
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• pp.1-20
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• 2022

This paper presents a social network analysis of changes in Home Economics education content loaded on YouTube before and after the outbreak of COVID-19. From January 1, 2008 to June 30, 2021, a basic analysis was conducted of 761 Home Economics education videos loaded on YouTube, using NetMiner 4.3 to analyze important keywords and the centrality of video titles and full texts. Before COVID-19, there were 164 Home Economics education videos posted on YouTube, increasing significantly to 597 following the emergence of the pandemic. In both periods, there was more middle school content than high school content. The content in the child-family field was the most, and the main keywords were youth and family. Before COVID-19, a performance evaluation indicated that the proportion of student content was high, whereas after the outbreak of the disease, teacher content increased significantly due to the effect of distance learning. However, compared with video use, the self-expression and participation of users were lower in both periods. The centrality analysis indicated that in the title, 'family' exhibited a high degree of both centrality and eigenvector centrality over the entire period. Degree centrality of the video title was found to be high in the order of class, online, family, management, etc. after the outbreak of COVID-19, and the connection of keywords was strong overall. Eigenvector centrality indicated that career, search, life, and design were influential keywords before COVID-19, while class, youth, online, and development were influential keywords after COVID-19.

• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.151-164
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• 2023

Purpose This study aims to analyze the current status of consumer awareness on Internet banks by conducting a full investigation and collecting user opinions presented on Google Play. After cateogorizing the current dissatisfaction, we would like to present not only the direction of the Internet bank service of but also the improvements of the platform. Design/methodology/approach Using opinion mining, subjectivity analysis, polarity analysis, and polarity information analysis of comments were conducted step by step to extract negative and positive keywords. The extracted keywords analyzed the weights of the frequently appearing positive and negative keywords using the TF-IDF model. Based on previous studies that negative information is more sensitive to positive information, we tried to confirm the connection, proximity, and mediation of negative keywords. Semantic Network Analysis (SNA) was used to visualize the connection relationship between the negative comment keywords of the three Internet banks. Findings Domestic Internet banks such as Kakao Bank, K-Bank, and Toss Bank have attracted a lot of attention even before they were established, and after establishment, they have secured a wide range of users through platforms that are completely different from existing banks. This study found out that the convenience of the app affects the opening and transaction of non-face-to-face accounts, which are characteristics of domestic Internet banks, which also affects the bank's business strategy. In addition, this study shows that the business characteristics of the company can be identified.

• Safety and Health at Work
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• 제12권2호
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• pp.272-276
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• 2021

This brief report aims to establish the keyword content of studies on occupational health and safety-the key framework of the world of work in the trade and health domain. Data were collected from the SCOPUS database, focusing on articles on occupational health and safety and related keywords, with an emphasis on abstracts and titles. Data were analyzed and summarized based on keywords included from the MeSH database. There were 24,499 manuscripts in the domain and 1,346 (5.40%) occupational health-related keywords, including those that overlapped. The most frequently referenced occupational health-related keyword was "occupational health" (452 articles), followed by "occupational safety" (141 articles). There were fewer keywords on occupational health in the trade and health literature. As the world of work has been prioritized because of the recent new normal of work life since the COVID-19 pandemic, examining the focus of occupational health priorities within the global perspective is crucial.

• 대한원격탐사학회지
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• 제33권2호
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• pp.189-199
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• 2017

본 연구는 텍스트 마이닝 기법을 활용하여 환경 분야에서 ICT의 활용 연구동향을 정량적으로 분석하였다. 이를 위해 환경 분야 키워드 38개, ICT 관련 키워드 16개를 바탕으로 국가과학기술정보센터(NDSL)에서 최근 20년(1996년-2015년)의 논문 359편을 수집하였다. 해당 논문을 대상으로 환경 분야 및 ICT 관련 자연어를 처리하여 말뭉치(Corpus)단위로 분류체계를 재구성하였다. 전술된 분류체계의 키워드를 바탕으로 텍스트 마이닝 분석 기법인 빈도 분석, 키워드 분석, 키워드 간 연관규칙을 확인하였다. 그 결과 '환경 일반' 및 '기후' 분야의 키워드 출현 빈도가 전체의 77 %, ICT는 '공공융합서비스' 및 '산업융합서비스'가 약 30 %의 비율을 차지하였다. 시계열 분석을 통해 환경 분야에서의 ICT 활용 연구는 최근 5년(2011년-2015년)사이에 급증하여 과거(1996년-2010년)과 비교하여 약 2배 이상 관련 연구가 증가된 것으로 나타났다. 키워드 간 연관 규칙을 생성하여 환경 분야를 기준으로 나타내었을 때, '환경 일반'은 16개, '기후'는 '14'개의 ICT 기반 기술을 주로 활용하고 있는 것으로 확인하였다.

• 대한건축학회연합논문집
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• 제21권5호
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• pp.109-116
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• 2019

The Seoul city recommended that old apartments be preserved, and as part of that, it decided to preserve some of the buildings for Jamsil Jugong, which was built in 1977, and Gaepo Jugong, which was constructed in 1981. The purpose of this study was to compare and review newspaper articles with two perspectives positive and negative about how the social perception of 'apartment trace remaining' was being constructed. By looking at the meaning of keywords delivered by newspaper articles and the interaction structure between keywords through the analysis of semantic networks, we analyzed how the media is pursuing an issue on the topic of preservation of architectural cultural heritage. The analysis results confirmed that there was a clear difference between positive and negative newspaper. Positive articles dealt with utilization from the point of view of keywords linked to preservation, and negative articles showed that keywords related to the property and backlash of residents linked to the policy of the Seoul Metropolitan Government were linked, leading to high negative public opinion.

• 복식문화연구
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• 제29권4호
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• pp.603-615
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• 2021

The aim of this study was to analyze the semantic network structure of keywords and the visual composition of images extracted from Instagram in relation to the multi-persona phenomenon with in fashion imagery, which has recently been attracting attention. To this end, the concept of a 'secondary character', which forms a separate identity from a 'main character' on various social media platforms as well as on the airwaves, was considered as the spread of multi-persona and #SecondaryCharacter on Instagram was investigated. 3,801 keywords were collected after crawling the data using Python and morphological analysis was undertaken using KoNLP. The semantic network structure was then examined by conducting a CONCOR analysis using UCINET and Netdraw to determine the top 50 keywords. The results were then classified into a total of 6 clusters. In accordance with the meaning and context of the keywords included in each cluster, group names were assigned : virtual characters, relationship with the main character, hobbies, daily record, N-job person, media and marketing. Image analysis considered the technical, compositional, and social styles of the media based on Gillian Rose's visual analysis method. The results determined that Instagram uses fashion images that virtualize one's face to produce multi-persona representation s that show various occupations, describe different types of hobbies, and depict situations pertaining to various social roles.

• Journal of Korea Trade
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• 제24권5호
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• pp.71-86
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• 2020

Purpose - This study investigates research trends by searching for English keywords and abstracts in 1,511 Korean journal articles in the Korea Citation Index from the 2002-2019 period using the term "Port." The study aims to lay the foundation for a more balanced development of port research. Design/methodology - Using abstract and keyword data, we perform frequency analysis and word embedding (Word2vec). A t-SNE plot shows the main keywords extracted using the TextRank algorithm. To analyze which words were used in what context in our two nine-year subperiods (2002-2010 and 2010-2019), we use Scattertext and scaled F-scores. Findings - First, during the 18-year study period, port research has developed through the convergence of diverse academic fields, covering 102 subject areas and 219 journals. Second, our frequency analysis of 4,431 keywords in 1,511 papers shows that the words "Port" (60 times), "Port Competitiveness" (33 times), and "Port Authority" (29 times), among others, are attractive to most researchers. Third, a word embedding analysis identifies the words highly correlated with the top eight keywords and visually shows four different subject clusters in a t-SNE plot. Fourth, we use Scattertext to compare words used in the two research sub-periods. Originality/value - This study is the first to apply abstract and keyword analysis and various text mining techniques to Korean journal articles in port research and thus has important implications. Further in-depth studies should collect a greater variety of textual data and analyze and compare port studies from different countries.