• Journal of Preventive Medicine and Public Health
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• v.41 no.6
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• pp.365-372
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• 2008

Objectives : There are very few researches on North Korea's academic activities. Furthermore, it is doubtful that the available data are reliable. This study investigated research activities and knowledge structure in the field of Preventive Medicine in North Korea with a network analysis using co-authors and keywords. Methods : The data was composed of the North Korean Journal of preventive medicine ranged from Vol. 1 of 1997 to Vol. 4 of 2006. It was the matrix of 1,172 articles by 1,567 co-authors. We applied R procedure for keywords abstraction, and then sought for the outcome of network forms by spring-KK and shrinking network. Results : To comprehend the whole networks explicitly demonstrated that the academic activities in North Korea s preventive medicine were predisposed to centralization as similar as South Korea's, but on the other aspect they were prone to one-off intermittent segmentation. The principal co-author networks were formulated around some outstanding medical universities seemingly in addition to possible intervention by major researchers. The knowledge structure of network was based on experimentation judging from keywords such as drug, immunity, virus detection, infection, bacteria, anti-inflammation, etc. Conclusions : Though North Korea is a socialist regime, there were network of academic activities, which were deemed the existence of inducive mechanism affordable for free research. Article keywords has laid greater emphasis on experiment-based bacterial defection, sustainable immune system and prevention of infection. The kind of trend was a consistent characteristic in preventive medicine of North Korea haying close correlation with Koryo medical science.

• Journal of Korean Society of Rural Planning
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• v.26 no.1
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• pp.39-49
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• 2020

The study analyzed the frequency of simultaneous occurrence of keywords presented in a total of 805 papers published in domestic journals from 1995 to 2019 by social network analysis(SNS) method, and examined core keywords of each period(5 years), in order to understand the research trends of the rural residential environment. The main results are as follows. First, as a result of the analysis of centrality, 'Community', 'Tourism' and 'Comprehensive Rural Village Development Project' were the top 3 keywords. Second, examined by each period, the top keywords are 'Eco Friendly' in 2000~2004, 'Tourism' in 2005~2009 and 2010~2014, 'Community' in 2015~2019. Third, comparing the structural characteristics of core keywords 2nd, 3rd, and 4th period, a network centering on 'Tourism' was clearly formed in the 2nd period. 'Tourism' was divided into 'Community' and a movement to form a separate group appeared in the 3rd period. In the 4th period, 'Community' was found to form a network without direct connection with 'Tourism'. The results of this study suggest the trend change of viewpoint for the rural area in the domestic research on rural residential environment. It has been confirmed that while the research had been carried out with the viewpoint of rural area as a 'tourist attraction' or 'sightseeing spot' for the urban citizens until the mid-2010s, in the research of late 2010s the viewpoint has settled down as a 'residential space' or 'space for new economic activities' of a variety of rural residents.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.67-82
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• 2019

The purpose of this study is to analyze the issues and perceptions of smart cities. First, based on the big data analysis platform, big data analysis on smart cities were conducted to derive keywords by year, word cloud, and frequency of generation of smart city keywords by time. Second, trend and flow by area were analyzed by reclassifying major keywords by year based on meta-keywords. Third, emotional recognition flow for smart cities and major emotional keywords were derived. While U-City in the past is mostly centered on creating infrastructure for new towns, recent smart cities are focusing on sustainable urban construction led by citizens, according to the analysis. In addition, it was analyzed that while infrastructure, service, and technology were emphasized in the past, management and methodology were emphasized recently, and positive perception of smart cities was growing. The study could be used as basic data for the past, present and future of smart cities in Korea at a time when smart city services are being built across the country.

• Journal of the Korean Society of Earth Science Education
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• v.11 no.2
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• pp.145-157
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• 2018

The purpose of this study is to analyze the research trends in domestic related to gifted counseling by utilizing Sementic analysis methods. For papers of gifted education in korea, KCI(Korea Citation Index) rated journals were selected 83 pieces published in journals were collected and the Sementic Network Analysis(SNA) way was utilizing for keyword frequency and Centrality Network Analysis throughout a variety of research articles using krkwic and Ucinet6.0. The results are as follows. first, the analysis appeared that the trends of paper keywords from highest frequency of appearance keyword in papers focused on four keywords: perfectionism, career, counseling, and the science gifted. second, Analysis of annual trends from 2001 to June 2018 showed that the top keywords were as follows: the gifted underachievers, the perfectionism, the gifted students of Science, and the science gifted students. the rising keywords were perfectionism, twice-exceptional students, and gifted parents, and the keywords of gifted students and general students showed a tendency to decrease. Consequently, gifted counseling research should be done from various perspectives.

• Journal of Korean Academy of Nursing
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• v.47 no.5
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• pp.600-612
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• 2017

Purpose: This study aimed to identify and compare hospice care research topics between Korean and international nursing studies using text network analysis. Methods: The study was conducted in four steps: 1) collecting abstracts of relevant journal articles, 2) extracting and cleaning keywords (semantic morphemes) from the abstracts, 3) developing co-occurrence matrices and text-networks of keywords, and 4) analyzing network-related measures including degree centrality, closeness centrality, betweenness centrality, and clustering using the NetMiner program. Abstracts from 347 Korean and 1,926 international studies for the period of 1998-2016 were analyzed. Results: Between Korean and international studies, six of the most important core keywords-"hospice," "patient," "death," "RNs," "care," and "family"-were common, whereas "cancer" from Korean studies and "palliative care" from international studies ranked more highly. Keywords such as "attitude," "spirituality," "life," "effect," and "meaning" for Korean studies and "communication," "treatment," "USA," and "doctor" for international studies uniquely emerged as core keywords in recent studies (2011~2016). Five subtopic groups each were identified from Korean and international studies. Two common subtopics were "hospice palliative care and volunteers" and "cancer patients." Conclusion: For a better quality of hospice care in Korea, it is recommended that nursing researchers focus on study topics of patients with non-cancer disease, children and family, communication, and pain and symptom management.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.157-163
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• 2018

People who use SNS publish their diverse ideas on SNS every day. The data posted on the SNS contains many people's thoughts and opinions. In particular, popular keywords served on Twitter compile the number of frequently appearing words in user posts and rank them. However, this method is sensitive to unnecessary data simply by listing duplicate words. The proposed method determines the ranking based on the topic of the word using the relationship diagram between words, so that the influence of unnecessary data is less and the main word can be stably extracted. For the performance comparison in terms of the descending keyword rank and the ratios of meaningless keywords among high rank 20 keywords, we make a comparison between the proposed scheme which is based on morphological analysis and PageRank, and the existing scheme which is based on the number of appearances. As a result, the proposed scheme and the existing scheme have included 55% and 70% of meaningless keywords among high rank 20 keywords, respectively, where the proposed scheme is improved about 15% compared with the existing scheme.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.41-46
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• 2003

This thesis composes separated words in text for extracting keywords from Japanese, proposes extracting indexing keywords which consist of a compound noun using words and sentences information with the rules in the sentences. It constructs generative rules of compound nouns to be based In dependence as a result of analysing character of keywords in the text not the same way as before. To hold other extracting keywords and the content of sentence, and suggest how to decide importance concerned some restriction and repetition of words about generative rules. To verify the validity of keywords extracting, we have used titles and abstracts from Japanese thesis 65 files about natural language and/or voice processing, and obtain 63% in outputting one in the top rank.

• The Research Journal of the Costume Culture
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• v.27 no.4
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• pp.398-413
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• 2019

This study empirically analyzed the influence of fashion brands' marketing issues on actual sales and consumer preference-focusing on evaluation trends of brands over time by using the theoretical background and big data provided through literature. This study examined the influence of three fashion brands (Balenciaga, Vetements, and Off-White) that have recently seen a drastic increase in the number of searched volumes through social networks. To identify the consumer-brand evaluations and trends and the marketing issues, the time period was divided into Groups A and B, which are from 2014 to 2015 and from 2016 to 2017, respectively. This study analyzed the frequency of overlapping keywords by using the R program to graphically visualize the changes over the timeline. Specifically, this analysis extracted data mainly related to bags, wallets and accessories for 2014-2015, but in 2016-2017, all four brands saw a vast increase in the frequency of searching product keywords related to clothing and footwear, and newly extracted ones were the top keywords. When analyzing the big data with these keywords as indicators, I confirmed that the products related to bags, wallets, and accessories were shifted to those related to apparel and footwear. Consumers previously recognized luxury brands such as Balenciaga as accessories-oriented brands that were focused on handbags and sunglasses, but now they are gaining popularity and recognition among consumers as a fashion brand.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.440-448
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• 2019

In this paper, we propose an ordering chatbot that can process multiple keywords based on recursive slot-filling method. In general, in case of an order service using chatbots, the whole order process is performed only according to the sequence defined by the developer. That is, among all the information needed for the whole order process, only one input can be processed at one time. In order to reduce processing step for the order, we propose a recursive slot-filling method which fills out multiple slots per one time by extracting multiple keywords. First, a keyword array for the order is created according to the order related information. Next, from the input sentence of a user, multiple keywords is extracted. Corresponding slots for a keyword array will be filled with the extracted keywords. Finally, recursive routine will be executed to fill out all the blank in the keyword array. The usability and validity of the proposed method will be shown from the implementation of a smartphone application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.401-406
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• 2019

In this paper, we propose a system for analyzing documents in XML format and in reports. The system extracts the paper or reports of keywords, shows them to the user, and then extracts the paragraphs containing the keywords by inputting the keywords that the user wants to search within the document. The system checks the frequency of keywords entered by the user, calculates weights, and removes paragraphs containing only keywords with the lowest weight. Also, we divide the refined paragraphs into 10 regions, calculate the importance of the paragraphs per region, compare the importance of each region, and inform the user of the main region having the highest importance. With these features, the proposed system can provide the main paragraphs with higher compression ratio than analyzing the papers or reports using the existing document analysis system. This will reduce the time required to understand the document.