• Journal of Digital Convergence
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• v.14 no.12
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• pp.237-243
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• 2016

Since realtime search keywords of portal sites are arranged in descending order by instant increasing rates of search numbers, they easily show issues increasing in interests for a short time. But they have the limits extracted different results by portal sites and not shown issues by a period. Thus, to find key issues from the whole realtime search keywords for certain period, and to show results of summarizing them and analyzing differences, is significant in providing the basis of understanding issues more practically and in maintaining consistency of them. This paper analyzes differences of week key issues extracted from week analysis of realtime search keywords provided by two typical portal sites. The results of experiments show that the portal group means of realtime search keywords by the independent t-test and the survival functions of realtime search keywords by the survival analysis are statistically significant differences.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.255-260
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• 2019

Real-time search keywords are not only limited to search keywords that are rapidly increasing interest in real-time, but also have a limitation that they are difficult to determine the sustainability as there is a difference in ranking between portal sites. Estimating sustainability for real-time search keywords is significant in terms of overcoming these limitations and providing some predictability. In particular, long-term search keywords that last for more than a month are of high value as long-lasting social issues. Therefore, in this paper, we analyze the interest based on the ranking of the real-time search keywords and the duration based on sustained weeks, days and hours of real-time search keywords by each portal site and the integrated portal site, and then estimating sustainability based on high level of interest and duration, and present a method to derive real-time search issues with high long-term sustainability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.758-764
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• 2018

Digital documents such as patents, scholarly papers and research reports have author keywords which summarize the topics of documents. Different documents are likely to describe the same topic if they share the same keywords. Document clustering aims at clustering documents to similar topics with an unsupervised learning method. However, it is difficult to apply to a large amount of documents event though the document clustering is utilized to in various data analysis due to computational complexity. In this case, we can cluster and connect massive documents using keywords efficiently. Existing bottom-up hierarchical clustering requires huge computation and time complexity for clustering a large number of keywords. This paper proposes an inverted index based bottom-up clustering for keywords and analyzes the results of clustering with massive keywords extracted from scholarly papers and research reports.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.538-549
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• 2019

Purpose: This study aimed to explore and compare the knowledge structure of pain management nursing research, between Korea and other countries, applying a text network analysis. Methods: 321 Korean and 6,685 international study abstracts of pain management, published from 2004 to 2017, were collected. Keywords and meaningful morphemes from the abstracts were analyzed and refined, and their co-occurrence matrix was generated. Two networks of 140 and 424 keywords, respectively, of domestic and international studies were analyzed using NetMiner 4.3 software for degree centrality, closeness centrality, betweenness centrality, and eigenvector community analysis. Results: In both Korean and international studies, the most important, core-keywords were "pain," "patient," "pain management," "registered nurses," "care," "cancer," "need," "analgesia," "assessment," and "surgery." While some keywords like "education," "knowledge," and "patient-controlled analgesia" found to be important in Korean studies; "treatment," "hospice palliative care," and "children" were critical keywords in international studies. Three common sub-topic groups found in Korean and international studies were "pain and accompanying symptoms," "target groups of pain management," and "RNs' performance of pain management." It is only in recent years (2016~17), that keywords such as "performance," "attitude," "depression," and "sleep" have become more important in Korean studies than, while keywords such as "assessment," "intervention," "analgesia," and "chronic pain" have become important in international studies. Conclusion: It is suggested that Korean pain-management researchers should expand their concerns to children and adolescents, the elderly, patients with chronic pain, patients in diverse healthcare settings, and patients' use of opioid analgesia. Moreover, researchers need to approach pain-management with a quality of life perspective rather than a mere focus on individual symptoms.

• Journal of Korean Clinical Nursing Research
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• v.25 no.1
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• pp.80-90
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• 2019

Purpose: The aim of this study was to identify the research trends of articles published in the Journal of Korean Clinical Nursing Research from 2000 to 2017 by a text network analysis using keywords. Methods: This study analyzed 600 articles. The R program was used for text mining that extracted frequency, centrality rank, and keyword network. Results: From 2000 to 2009, keywords with high-frequency were 'nurse', 'pain', 'anxiety', 'knowledge', 'attitude', and so on. 'Pain', 'nurse', and 'knowledge' showed a high centrality. 'Fatigue' showed no high frequency but a high centrality. Keywords such as 'nurse', 'knowledge', and 'pain' also showed high frequency and centrality between 2010 and 2017. 'Hemodialysis' and 'intensive care unit' were added to keywords with high frequency and centrality during the period. Conclusion: The frequency and centrality of keywords such as 'nurse', 'pain', 'knowledge', 'hemodialysis', and 'intensive care unit' reflect the research trends in clinical nursing between 2000 and 2017. Further studies need to expand the keyword networks by connecting the main keywords.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.17-27
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• 2018

In this paper, the research trends of port hinterland from 1990 to 2018 were analyzed periodically using the Social Network Analysis (SNA) method. The data were collected from major academic journals and totally 116 papers were identified for analysis. The results of the analysis showed that in the first period (1990-1999), keywords can be listed as "containerization", "transport infrastructure" and developed countries related keywords like "Italy", "Canada" and "Germany". The results of the second period (2000-2009) were originated from keywords such as "regionalization", "competitiveness", "Asian consolidation" and "technology". In the third period (2010-2018), the results were derived from keywords such as "intermodal transport", "dry port", "container" and container related keywords and "shipping" and shipping related keywords. We could see the studies of port hinterland are becoming more systematic and integrated. This study provides some important implications for both academic, and industrial viewpoints, and it is helpful to understand the research concentration.

• 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.

• Journal of Research and Publication Ethics
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• v.2 no.1
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• pp.19-24
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• 2021

Purpose: The purpose of this study is to check the direction of KODISA's pursuit of complex and convergence studies by confirming the research trends of KODISA's direct academic journals such as JDS, JIDB, JBEES and JAFEB. To this end, we tried to compare and confirm the research trends of the papers in four academic journals targeting keywords. Research Design, data and methodology: The analysis was conducted from 2014 to 2020 on 867 papers from JDS, 315 papers from JIDB, 120 papers from JBEES, and 867 papers based on the publication year of the most recently published journal from JAFEB. For the analysis, frequency analysis, word crowding, topic modeling, and frequency analysis by applying weights for each year group were performed on the keywords crawled using Python. Results: The results of frequency analysis showed that each journal is properly oriented toward its target direction. In addition, it was confirmed that the results of topic modeling significantly reflected the results of frequency analysis. Finally, it could be concluded that the results of frequency analysis using the weights of keywords by year group were also developing in the direction the target journals were analyzed. Specifically, in the case of JDS, 20 keywords such as Service Quality, Distribution were found to increase continuously according to the year group. Meanwhile, the keywords that continued to increase according to JIDB's year group were India, Social Capital, and Job Stress. The keywords that continued to increase according to the year group of JBEES were Micro Finance Institutions and Microfinance, and the keywords that of JAFEB were confirmed to be Vietnam and Service Quality. Conclusion: It was confirmed that KODISA's direct management journals responded appropriately to convergence issues. In particular, it was confirmed that researches in various fields of JDS are continuously increasing. However, it seems that JIDB needs to deal with various issues additionally in the service industry field and JBEES in the environment field. Finally, it was found that JAFEB needs to be wary of the relatively low level of interest in some countries such as Kazakhstan and India in recent years.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.23-35
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• 2020

This study aims to examine trends in the IoT field by collecting and analyzing domestic papers on IoT that will have a great impact across industries and society. The survey period for this study was from 2015 to 2019, and the domestic papers on the IoT were collected using Naver's Academic Information. We extracted the keywords with high frequency from the domestic papers collected by the period and performed the centrality analysis to identify the central keywords among the keywords with high frequency. In terms of keyword frequency, 'sensor' and 'security' from 2015 to 2017 appeared as the top keywords with high frequency. From 2017, 'car' and 'intelligence' appeared as the top keywords with high frequency. In terms of keyword centrality, 'security' and 'sensor' from 2015 to 2016 appeared as highly centralized keywords. From 2017, 'intelligence', 'car' and 'industrial revolution' appeared as highly centralized keywords.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.91-100
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• 2021

PURPOSE: This study analyzed the trends and characteristics of shoulder rehabilitation research through keyword analysis, and their relationships were modeled using text mining techniques. METHODS: Abstract data of 10,121 articles in which abstracts were registered on the MEDLINE of PubMed with 'shoulder' and 'rehabilitation' as keywords were collected using python. By analyzing the frequency of words, 10 keywords were selected in the order of the highest frequency. Word-embedding was performed using the word2vec technique to analyze the similarity of words. In addition, the groups were classified and analyzed based on the distance (cosine similarity) through the t-SNE technique. RESULTS: The number of studies related to shoulder rehabilitation is increasing year after year, keywords most frequently used in relation to shoulder rehabilitation studies are 'patient', 'pain', and 'treatment'. The word2vec results showed that the words were highly correlated with 12 keywords from studies related to shoulder rehabilitation. Furthermore, through t-SNE, the keywords of the studies were divided into 5 groups. CONCLUSION: This study was the first study to model the keywords and their relationships that make up the abstracts of research in the MEDLINE of Pub Med related to 'shoulder' and 'rehabilitation' using text-mining techniques. The results of this study will help increase the diversifying research topics of shoulder rehabilitation studies to be conducted in the future.