• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.721-732
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• 2021

This study analyzed keywords for agricultural R&D using the textmining method to examine the trend of agricultural R&D. Data used for the analysis included R&D project information provided by NTIS, and the research and development step by year from 2003 to 2018 were classified and applied. The TF-IDF approach was used as the analysis method, and ranking was derived based on score. Furthermore, we analyzed by grouping for similar keywords. The main analysis results are as follows. First, agricultural R&D trends are changing according to the introduction of new technologies and changes in the external environment. Second, keyword changes appeared with a time lag in the R&D step. The main keywords are changing in the order of basic research - applied research - development research. Third, the main keyword of agricultural R&D was 'rice.' However, the direction and purpose of the research were changing according to changes in the domestic and foreign agricultural environments.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.55-68
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• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.

• 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.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.351-373
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• 2023

This study aimed to analyze and collect research trends of archival management standards in Korea. For this purpose, keywords from the titles, author keywords, and abstracts of papers related to records management standards were statistically analyzed to investigate the major keywords with high-frequency. Network analysis with high frequency keywords was also conducted to identify the subject areas of research in archival management standards. The analysis period is from 2000 to the present, and a total of 212 papers were collected from domestic academic paper search sites such as RISS and ScienceON. As a result of the analysis, from 2000 to 2010, OAIS for archive design, digital record preservation with OAIS, and analysis on ISO standards were mainly conducted in research areas. From 2011 until now, records management certification and ISAD(G)'s conversion to RiC emerged as new research areas. This study will be expected to be basic data to understand research trends in records management standards in Korea and to be a reference for research on records management standards studies.

• Korean Management Science Review
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• v.23 no.3
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• pp.77-93
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• 2006

In the highly competitive world, there has been a concomitant increase in the need for the research and planning methodology, which can perform an advanced assessment of technological opportunities and an early Perception of threats and possibilities of the emerging technology according to the nation's economic and social status. This research is aiming to provide indicators and visualization methods to measure the latest research trend and aspect underlying scientific and technological documents to researchers and policy planners using 'Co-word Analysis' Organic light emitting diodes(OLED) is an emerging technology in various fields of display and which has a highly prospective market value. In this paper, we presented an analysis on OLED. Co-word analysis was employed to reveal patterns and trends in the OLED fields by measuring the association strength of terms representatives of relevant publications or other texts produced in the OLED field. Data were collected from SCI and the critical keywords could De extracted from the author keywords. These extracted keywords were further standardized. In order to trace the dynamic changes in the OLED field, we presented a variety of technology mapping. The results showed that the OLED field has some established research theme and also rapidly transforms to embrace new themes.

• Child Health Nursing Research
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• v.25 no.4
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• pp.458-467
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• 2019

Purpose: The purpose of this study was to identify human papillomavirus (HPV) vaccination research trends by visualizing a keyword network. Methods: Articles about HPV vaccination were retrieved from the PubMed and Web of Science databases. A total of 1,448 articles published in 2006~2016 were selected. Keywords from the abstracts of these articles were extracted using the text mining program WordStat and standardized for analysis. Sixty-four keywords out of 287 were finally chosen after pruning. Social network analysis using NetMiner was applied to analyze the whole keyword network and the betweenness centrality of the network. Results: According to the results of the social network analysis, the central keywords with high betweenness centrality included "health education", "health personnel", "parents", "uptake", "knowledge", and "health promotion". Conclusion: To increase the uptake of HPV vaccination, health personnel should provide health education and vaccine promotion for parents and adolescents. Using social media, governmental organizations can offer accurate information that is easily accessible. School-based education will also be helpful.

• Educational Technology International
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• v.21 no.2
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• pp.193-216
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• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.