• Journal of Korean Library and Information Science Society
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• v.48 no.1
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• pp.207-225
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• 2017

The aim of this study was to discover various Library and Information Science (LIS) research areas by examining similarities and differences between LIS journals in terms of keyword characteristics. To conduct this study, for the years from 2004 to 2016, the keywords of 6 international journals were downloaded from Scopus database (http://www.scopus.com), and the keywords of 4 Korean journals were downloaded from the RISS database (http://www.riss.co.kr). The characteristics of keywords were investigated by examining frequently used keywords and frequently used distinctive keywords pertaining to international and Korean journals. The distinctive keywords are referred to as the keywords that appear in one domain but not in another. The result of this study indicated the following: a) a frequency analysis of the keywords showed major research themes and unique traits concerning Korea. b) In general, the keywords used in Korean journals frequently reflected the library as a major subject area of research, while keywords used in international journals reflected bibliometrics and information retrieval as major subject areas of research. c) The overarching themes of each created dataset were clearly noticeable in frequently used distinctive keywords. d) Some keywords were bound by a nation or by a region due to their scope of usage. The important implication of this study is that both most frequently used keywords and most frequently used distinctive keywords seemed to adequately represent the LIS subject areas.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.101-107
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• 2020

This paper aims to compare and analyze the major keywords according to the political orientation of progressive and conservative newspapers by utilizing the big data of the four major domestic daily newspapers related to COVID-19, which has entered a long-term war. To this end, 93,917 news reports from Jan. 20 to Sept. 15, 2020 were divided into four stages and the major keywords of the four newspapers were implemented and analyzed in WordCloud. According to the analysis, the conservative newspaper focused on the government's response, criticism, and China's responsibility by mentioning the keywords "government," "president," "state of affairs" and "mask" more than the progressive newspaper, while the progressive newspaper uses keywords that emphasize the seriousness of the disease and the occurrence of a dangerous situation. The Chosun Ilbo found that the use of various keywords during the massive outbreak of collective infections (2.18-5.15), and that the JoongAng Ilbo used keywords criticizing government policies in relation to reports of infectious diseases such as COVID-19, but also used keywords that emphasize the seriousness of diseases used by progressive newspapers and the occurrence of dangerous situations.

• The Journal of Information Systems
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• v.28 no.4
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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.

• Journal of Korean Academy of Nursing
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• v.35 no.7
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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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.

• The Korean Journal of Emergency Medical Services
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• v.16 no.2
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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.

• Korean Journal of Veterinary Service
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• v.44 no.4
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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.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.17-24
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• 2018

One of the major issues in big data processing is extracting keywords from internet and using them to process the necessary information. Most of the proposed keyword extraction algorithms extract keywords using search function of a large portal site. In addition, these methods extract keywords based on already posted or created documents or fixed contents. In this paper, we propose a KAES(Keyword Advertisement Extraction System) system that helps the potential shopping keyword marketing to extract issue keywords and related keywords based on dynamic instant messages such as various issues, interests, comments posted on SNS. The KAES system makes a list of specific accounts to extract keywords and related keywords that have most frequency in the SNS.

• Women's Health Nursing
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• v.29 no.2
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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.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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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.