• 제목/요약/키워드: Related Keywords

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Exploration of Research Trends in The Journal of Distribution Science Using Keyword Analysis

  • YANG, Woo-Ryeong
    • 산경연구논집
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    • 제10권8호
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    • pp.17-24
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    • 2019
  • Purpose - The purpose of this study is to find out research directions for distribution and fusion and complex field to many domestic and foreign researchers carrying out related academic research by confirming research trends in the Journal of Distribution Science (JDS). Research Design, Data, and Methodology - To do this, I used keywords from a total of 904 papers published in the JDS excluding 19 papers that were not presented with keywords among 923. The analysis utilized word clouding, topic modeling, and weighted frequency analysis using the R program. Results - As a result of word clouding analysis, customer satisfaction was the most utilized keyword. Topic modeling results were divided into ten topics such as distribution channels, communication, supply chain, brand, business, customer, comparative study, performance, KODISA journal, and trade. It is confirmed that only the service quality part is increased in the weighted frequency analysis result of applying to the year group. Conclusion - The results of this study confirm that the JDS has developed into various convergence and integration researches from the past studies limited to the field of distribution. However, JDS's identity is based on distribution. Therefore, it is also necessary to establish identity continuously through special editions of fields related to distribution.

마을형 친환경지표 설정을 위한 친환경관련 문헌 조사 연구 (Characteristics of Literature Related to Environmental Friendliness for a Village-focused Green Index)

  • 변경화;유창균
    • 한국주거학회논문집
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    • 제25권2호
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    • pp.79-87
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    • 2014
  • The purpose of this study is to research the characteristics of literature related to environmentally friendly for a village-focused green index. In order to make an assessment, keywords relating to green architecture were selected: environmental friendliness, ecology, sustainable, Noksaek (Green in Korean), green, and environmentally friendly. In addition, three keywords defining the scope of space were also selected: building, village, and city. Quantitative changes and contents of articles containing the keywords were analyzed. The result is as follows. First, 'sustainable' and 'ecology' were the terms most frequently used as parts of subjects and titles, respectively. Second, the studies relating to green architecture focused on villages mostly examine the actual conditions of the villages; criteria for environmental friendliness, analyses and evaluation of the environmentally friendly features of the village; and ways to establish a green, ecological, and sustainable village. Finally, when it came to establishing a village-focused green index, the environment, resources, and energy are shown to be very important elements. In addition, the term 'ecology' in a green index is shown to be significant for the management of the natural environment.

A bio-text mining system using keywords and patterns in a grid environment

  • Kwon, Hyuk-Ryul;Jung, Tae-Sung;Kim, Kyoung-Ran;Jahng, Hye-Kyoung;Cho, Wan-Sup;Yoo, Jae-Soo
    • 한국산업정보학회:학술대회논문집
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    • 한국산업정보학회 2007년도 춘계학술대회
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    • pp.48-52
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    • 2007
  • As huge amount of literature including biological data is being generated after post genome era, it becomes difficult for researcher to find useful knowledge from the biological databases. Bio-text mining and related natural language processing technique are the key issues in the intelligent knowledge retrieval from the biological databases. We propose a bio-text mining technique for the biologists who find Knowledge from the huge literature. At first, web robot is used to extract and transform related literature from remote databases. To improve retrieval speed, we generate an inverted file for keywords in the literature. Then, text mining system is used for extracting given knowledge patterns and keywords. Finally, we construct a grid computing environment to guarantee processing speed in the text mining even for huge literature databases. In the real experiment for 10,000 bio-literatures, the system shows 95% precision and 98% recall.

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Research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis

  • Kang, Kyung-Ah;Han, Suk Jung;Chun, Jiyoung;Kim, Hyun-Yong
    • Child Health Nursing Research
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    • 제27권3호
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    • pp.201-210
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    • 2021
  • Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI). Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling. Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life". Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

키워드 네트워크 분석을 이용한 MIS 교과정보와 NCS 기반 빅데이터 분석 직무역량에 대한 연구 (A Study on MIS Curriculum and NCS-based Big Data Analysis Job Competency Using Keyword Network Analysis)

  • 이태원;성행남;김은정
    • 한국정보시스템학회지:정보시스템연구
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    • 제29권4호
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    • pp.101-121
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    • 2020
  • Purpose The purpose of this study is to understand the current status of MIS curriculum and to find ways to improve it. In addition, the results of the research can be used as basic data for improving MIS curriculum. Design/methodology/approach A research framework was designed to derive research results using the keyword network analysis method of this study: 1) Keywords were extracted based on the six units of the big data analysis job competency. 2) And based on the extracted keywords, the relationship between the keywords and MIS curriculum for each university was identified. Findings In the MIS curriculum information of a few universities, education related to big data analysis was conducted. 1) In the MIS curriculum of a few universities, education related to big data analysis was conducted. However, MIS curriculum of the university, which is the subject of analysis, education focused on concepts and theory rather than practical education was conducted. 2) And it was confirmed that there is a difference from the education required by the industry.

Big Data Analysis of the Women Who Score Goal Sports Entertainment Program: Focusing on Text Mining and Semantic Network Analysis.

  • Hyun-Myung, Kim;Kyung-Won, Byun
    • International Journal of Internet, Broadcasting and Communication
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    • 제15권1호
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    • pp.222-230
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    • 2023
  • The purpose of this study is to provide basic data on sports entertainment programs by collecting data on unstructured data generated by Naver and Google for SBS entertainment program 'Women Who Score Goal', which began regular broadcast in June 2021, and analyzing public perceptions through data mining, semantic matrix, and CONCOR analysis. Data collection was conducted using Textom, and 27,911 cases of data accumulated for 16 months from June 16, 2021 to October 15, 2022. For the collected data, 80 key keywords related to 'Kick a Goal' were derived through simple frequency and TF-IDF analysis through data mining. Semantic network analysis was conducted to analyze the relationship between the top 80 keywords analyzed through this process. The centrality was derived through the UCINET 6.0 program using NetDraw of UCINET 6.0, understanding the characteristics of the network, and visualizing the connection relationship between keywords to express it clearly. CONCOR analysis was conducted to derive a cluster of words with similar characteristics based on the semantic network. As a result of the analysis, it was analyzed as a 'program' cluster related to the broadcast content of 'Kick a Goal' and a 'Soccer' cluster, a sports event of 'Kick a Goal'. In addition to the scenes about the game of the cast, it was analyzed as an 'Everyday Life' cluster about training and daily life, and a cluster about 'Broadcast Manipulation' that disappointed viewers with manipulation of the game content.

텍스트 마이닝 기법을 이용한 환경 분야의 ICT 활용 연구 동향 분석 (A Study on Environmental research Trends by Information and Communications Technologies using Text-mining Technology)

  • 박보영;오관영;이정호;윤정호;이승국;이명진
    • 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.

토픽모델링을 활용한 해운물류 뉴스 분석 (Analysis of Shipping and Logistics News Articles using Topic Modeling)

  • 윤희영;곽일엽
    • 무역학회지
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    • 제46권4호
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    • pp.61-76
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    • 2021
  • This study focuses on three logistics-related news (Logistics Newspaper, Korea Shipping Gadget, and Korea Shipping Newspaper) in order to present changes in logistics issues, centering on Corona 19, which has recently had the greatest impact in the world. For data collection, two-year news articles in 2019 and 2020 (title, article, content, date, article classification, article URL) were collected through web crawling (using Python's BeautifulSoup, requests module) on the homepages of three representative logistics-related media companies. As for the data analysis methods, fundamental statistical analysis, Latent Dirichlet Allocation (LDA) for topic modeling, and Scattertext were performed. The analysis results were as follows. First, among the three news media related to logistics, the Korea Shipping Newspaper was carrying out the most active media activities. Second, through topic modeling with LDA, eight logistics-related topics were identified, and keywords and significant issues of each topic were presented. Third, the keywords were visually expressed through Scattertext. This is the first study to present changes in the logistics field, focusing on articles from representative logistics-related media in 2019 and 2020. In particular, 2019 and 2020 can be divided into before and after the outbreak of Corona 19, which has had a great impact not only on the logistics field but also on our lives as a whole. For future work, a multi-faceted approach is required, such as comparative studies of logistics issues between countries or presenting implications based on long-term time-series articles.

LDA 알고리즘을 이용한 프랜차이즈 연구 동향에 대한 토픽모델링 분석 (Topic Modeling Analysis of Franchise Research Trends Using LDA Algorithm)

  • 양회창
    • 한국프랜차이즈경영연구
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    • 제12권4호
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    • pp.13-23
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    • 2021
  • Purpose: This study aimed to derive clues for the franchise industry to overcome difficulties such as various legal regulations and social responsibility demands and to continuously develop by analyzing the research trends related to franchises published in Korea. Research design, data and methodology: As a result of searching for 'franchise' in ScienceON, abstracts were collected from papers published in domestic academic journals from 1994 to June 2021. Keywords were extracted from the abstracts of 1,110 valid papers, and after preprocessing, keyword analysis, TF-IDF analysis, and topic modeling using LDA algorithm, along with trend analysis of the top 20 words in TF-IDF by year group was carried out using the R-package. Results: As a result of keyword analysis, it was found that businesses and brands were the subjects of research related to franchises, and interest in service and satisfaction was considerable, and food and coffee were prominently studied as industries. As a result of TF-IDF calculation, it was found that brand, satisfaction, franchisor, and coffee were ranked at the top. As a result of LDA-based topic modeling, a total of 12 topics including "growth strategy" were derived and visualized with LDAvis. On the other hand, the areas of Topic 1 (growth strategy) and Topic 9 (organizational culture), Topic 4 (consumption experience) and Topic 6 (contribution and loyalty), Topic 7 (brand image) and Topic 10 (commercial area) overlap significantly. Finally, the trend analysis results for the top 20 keywords with high TF-IDF showed that 10 keywords such as quality, brand, food, and trust would be more utilized overall. Conclusions: Through the results of this study, the direction of interest in the franchise industry was confirmed, and it was found that it was necessary to find a clue for continuous growth through research in more diverse fields. And it was also considered an important finding to suggest a technique that can supplement the problems of topic trend analysis. Therefore, the results of this study show that researchers will gain significant insights from the perspectives related to the selection of research topics, and practitioners from the perspectives related to future franchise changes.

Patent Technology Trends of Oral Health: Application of Text Mining

  • Hee-Kyeong Bak;Yong-Hwan Kim;Han-Na Kim
    • 치위생과학회지
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    • 제24권1호
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    • pp.9-21
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    • 2024
  • Background: The purpose of this study was to utilize text network analysis and topic modeling to identify interconnected relationships among keywords present in patent information related to oral health, and subsequently extract latent topics and visualize them. By examining key keywords and specific subjects, this study sought to comprehend the technological trends in oral health-related innovations. Furthermore, it aims to serve as foundational material, suggesting directions for technological advancement in dentistry and dental hygiene. Methods: The data utilized in this study consisted of information registered over a 20-year period until July 31st, 2023, obtained from the patent information retrieval service, KIPRIS. A total of 6,865 patent titles related to keywords, such as "dentistry," "teeth," and "oral health," were collected through the searches. The research tools included a custom-designed program coded specifically for the research objectives based on Python 3.10. This program was used for keyword frequency analysis, semantic network analysis, and implementation of Latent Dirichlet Allocation for topic modeling. Results: Upon analyzing the centrality of connections among the top 50 frequently occurring words, "method," "tooth," and "manufacturing" displayed the highest centrality, while "active ingredient" had the lowest. Regarding topic modeling outcomes, the "implant" topic constituted the largest share at 22.0%, while topics concerning "devices and materials for oral health" and "toothbrushes and oral care" exhibited the lowest proportions at 5.5% each. Conclusion: Technologies concerning methods and implants are continually being researched in patents related to oral health, while there is comparatively less technological development in devices and materials for oral health. This study is expected to be a valuable resource for uncovering potential themes from a large volume of patent titles and suggesting research directions.