• 한국도서관정보학회지
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• 제48권1호
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• pp.207-225
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• 2017

본 연구의 목적은 키워드 특징 면에서 문헌정보 저널에서 나타나는 유사점과 차이점을 조사하여 다양한 문헌 정보학 연구 영역을 발견하는 데 있다. 이 연구를 수행하기 위해 2004 년부터 2016 년까지 네 개의 한국 저널의 키워드가 RISS 데이타베이스에서 수집 되었고(http://www.riss.co.kr) 그리고 여섯 개의 국제저널의 키워드가 SCOPUS 데이타베이스에서 수집 되었다(http://www.scopus.com). 키워드의 특징은 한국 및 국제저널에 관하여서 자주 사용 되었던 키워드와 자주 사용되었던 독특한 키워드를 검증하는 연구이었다. 독특한 키워드란 한 분야에서는 나타나지만 다른 분야에서는 나타나지 않는 키워드를 말한다. 이 연구의 결과는 다음과 같다. 가) 키워드 빈도 분석 결과는 한국의 문헌정보 학의 연구주제와 연구특색을 보여 주는 것으로 나타났다. 나) 일반적으로 한국 저널에서 사용 된 키워드는 도서관과 관련된 주제의 영역을 나타냈고, 국제 저널에 사용되는 키워드는 서지 측정법과 관련된 주제 영역을 나타냈다. 다) 빈번히 사용되었던 독특한 키워드에서도 이러한 전반적인 연구 테마를 명백히 나타냈다. 라) 어떤 키워드는 쓰이는 범위가 한 국가나 지역으로 한정되어 있는 것으로 나타냈다. 이 연구의 중요한 시사점은 가장 자주 사용되는 키워드와 가장 자주 사용되는 독특한 키워드는 둘 다 문헌정보 학의 주제 영역을 적절하게 반영하고 있는 것으로 보인다는 것이다.

• 한국컴퓨터정보학회논문지
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• 제25권12호
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• pp.101-107
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• 2020

본 논문은 장기전에 접어든 코로나19와 관련한 국내 주요 4개 일간지의 뉴스 빅데이터(빅카인즈)를 활용하여 진보와 보수신문의 정치적 성향 등에 따른 주요 키워드를 비교 분석하는 것을 목적으로 한다. 이를 위해 2020년 1월 20일부터 9월 15일까지 보도된 93,917건의 뉴스를 4단계로 구분하여 4개 신문사의 주요 키워드를 워드클라우드로 구현하여 분석하였다. 분석 결과, 보수신문은 진보신문보다 '정부', '대통령', '사태', '마스크' 키워드를 더 많이 언급함으로써 정부의 대응과 비판, 중국의 책임 등에 주목하였으며, 진보신문은 질병의 심각성과 위험 상황 발생을 강조하는 키워드를 많이 사용하는 것으로 나타났다. 조선일보는 대규모 집단감염 발생(2.18~5.15)기에 다양한 키워드의 사용으로 다양성을 나타내기도 하였으며 특히, 중앙일보가 코로나19와 같은 감염병 보도와 관련해서는 정부 정책을 비판하는 키워드를 사용하기도 하지만 진보신문이 사용하는 질병의 심각성과 위험한 상황 발생을 강조하는 키워드도 함께 사용한다는 점을 밝혀냈다.

• 한국정보시스템학회지:정보시스템연구
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• 제28권4호
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• pp.271-291
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• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• 대한간호학회지
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• 제35권7호
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• pp.1420-1425
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• 2005

Purpose: We try to disclose how much the keywords of the papers from the Journal of the Korean Academy of Nursing coincide with MeSH terminologies and to understand the major subjects of the recent nursing research in Korea from keywords. Methods: Keywords of journals were extracted and compared with MeSH terms. The frequency of the appearance of each keyword was sorted by a descending order. Results: Coincidence rate of 1,235 keywords with MeSH terms was $51.6\%$. Out of them, depression, elderly, stress, self efficacy, quality of life, exercise, middle-aged women, and women appeared most frequently in descending order. Conclusion: Coincidence rate of the keywords with MeSH terms was at an acceptable level, however to improve it, the education of submitters and editorial board members are required, as well as the copy editor, to take a role in checking keywords. To infer the subjects of the research from keywords might well represent the recent topics of research work.

• 산업경영시스템학회지
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• 제46권4호
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• pp.246-262
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• 2023

The purpose of this study is to identify the major peacekeeping activities that the Korean armed forces has performed from the past to the present. To do this, we collected 692 press releases from the National Defense Daily over the past 20 years and performed topic modeling and social network analysis. As a result of topic modeling analysis, 112 major keywords and 8 topics were derived, and as a result of examining the Korean armed forces's peacekeeping activities based on the topics, 6 major activities and 2 related matters were identified. The six major activities were 'Northeast Asian defense cooperation', 'multinational force activities', 'civil operations', 'defense diplomacy', 'ceasefire monitoring group', and 'pro-Korean activities', and 'general troop deployment' related to troop deployment in general. Next, social network analysis was performed to examine the relationship between keywords and major keywords related to topic decision, and the keywords 'overseas', 'dispatch', and 'high level' were derived as key words in the network. This study is meaningful in that it first examined the topic of the Korean armed forces's peacekeeping activities over the past 20 years by applying big data techniques based on the National Defense Daily, an unstructured document. In addition, it is expected that the derived topics can be used as a basis for exploring the direction of development of Korea's peacekeeping activities in the future.

• 한국응급구조학회지
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• 제16권2호
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• pp.43-51
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• 2012

Purpose : We try to disclose how much the keywords of the papers from the Korean Journal of Emergency Medical Services with Medical Subject Headings(MeSH) terminologies and to understand the major subjects of the recent emergency medical technology research in Korea from keywords. Methods : We analyzed keywords from 524 articles of the Korean Journal of Emergency Medical Services that were published between 2005 and 2011. We investigated frequently used keywords and what percentages of keywords agree with MeSH terms using the MeSH browser. Results : There were on average 3.2 keywords per article. The most frequent key words were AED, Attitude, Cardiopulmonary Resuscitation, CPR, EMT, EMT students, External Defibrillator, Job satisfaction, Knowledge, 119 EMT in order. The number of terms in precise agreement with MeSH headings was 101(19.3%); 327 terms(62.4%) were not found in the MeSH browser and 96 terms(18.3%) partially matched MeSH terms. Conclusion : Many keywords used in the Korean Journal of Emergency Medical Services did not agree with MeSH terms. We conclude that contribution rules should be using MeSH terms and authors should be educated in the proper use of MeSH terms in their research and subsequent publication.

• 한국동물위생학회지
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• 제44권4호
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• pp.257-270
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• 2021

Since African swine fever (ASF) spread to East Asia, a fatal crisis has occurred in the global pig industry, because Asia is dominant in pig production. Although some studies conducted bibliometric analysis on ASF, few studies compared research networks, and identified subthemes by major keywords. To fill this gap, this study identified the knowledge structure network of the research, its influence, and core research themes by utilizing the bibliometric analysis of 337 ASF-related journal articles over 50 years from 1970 to 2020 on the Web of Science. The result indicated that papers are mainly published in the fields of veterinary science, virology, microbiology, infectious disease and applied microbiology, and in particular, the fields of veterinary science and virology showed unrivaled weights as they account for 73.40%. With regard to cooperative relationships, European countries such as the UK, Germany, Italy, and Denmark, centered on Spain, are actively contributing to the ASF research. China, France, Thailand, Japan, Vietnam, and South Korea are leading research cooperation, centering on the United States. In the early stage of the studies, major keywords appeared to be related to outbreaks, quarantine and diagnosis, and in the middle stage, the keywords were expanded to a wide range of pig diseases. Recently, the keywords are becoming more diverse towards antibodies, cross-border transmission and disease monitoring. Based on data on major keywords related to ASF, this study proposed discussions and implications for activating ASF research including genotype, protein, vaccine, diagnosis, defense against infection and epidemiological investigation.

• 한국융합학회논문지
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• 제9권7호
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• pp.17-24
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• 2018

빅데이터 처리 분야에서 중요한 이슈 중 하나는 인터넷의 주요 키워드를 추출하고 이것을 이용하여 필요한 정보를 가공하는 것이다. 현재까지 제안된 대부분의 키워드 추출 방법들은 대형 포털 사이트의 검색기능을 기반으로 이미 게시된 글이나 작성된 문서 또는 고정된 내용에 기반하고 있다. 본 논문에서는 SNS에 게시되는 다양한 이슈, 대화, 관심 분야, 의견 등 동적인 메시지를 기반으로 이슈 키워드 및 연관 키워드를 추출하여 잠재적 쇼핑 연관 키워드 광고 마케팅에 도움을 주는 시스템(KAES: Keyword Advertisement Extraction System based on SNS)을 개발한다. KAES 시스템은 특정 계정 리스트를 작성하여 SNS에서 빈도수가 가장 많은 핵심 키워드 및 연관 키워드를 추출한다.

• 여성건강간호학회지
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• 제29권2호
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• 대한안전경영과학회지
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• 제17권4호
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• pp.335-342
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• 2015

The increasing interests on patents have led many individuals and companies to apply for many patents in various areas. Applied patents are stored in the forms of electronic documents. The search and categorization for these documents are issues of major fields in data mining. Especially, the keyword extraction by which we retrieve the representative keywords is important. Most of techniques for it is based on vector space model. But this model is simply based on frequency of terms in documents, gives them weights based on their frequency and selects the keywords according to the order of weights. However, this model has the limit that it cannot reflect the relations between keywords. This paper proposes the advanced way to extract the more representative keywords by overcoming this limit. In this way, the proposed model firstly prepares the candidate set using the vector model, then makes the graph which represents the relation in the pair of candidate keywords in the set and selects the keywords based on this relationship graph.