• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.207-214
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• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• Journal of the East Asian Society of Dietary Life
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• v.24 no.3
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• pp.335-350
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• 2014

Compared to the large numbers of studies on the diabetes, hyperlipidemia and cancer therpeutic effects of ginseng, the anti-obese effect and mechanisms of ginsengs have not been studied as much. To determine the effects of ginseng on obesity, 14 keywords (ginseng, ginsenoside, obesity, weight, fat, diet, overeat, appetite, lipid, 3T3-L1, adipocyte, food intake, adipogenesis and lipolysis) were combined in searching a database. Fifty-six articles published from 1983 to 2012 as well as 656 patents registered until Aug $17^{th}$, 2012, were screened for anti-obese effects of ginseng. In the classification of experimental methods, 16 papers on 3T3-L1 cells, 38 papers on animals and three papers on human were reviewed. In terms of obese mechanisms of action, the most commonly used biomarkers were in order of lipid profiles > weight change > blood glucose > adipocytokine. Most ginseng studies on obesity focused on AMPK, $PPAR{\gamma}$, GLUT-4, PI3K and SREBP-1. Korean white ginseng extracts and Re repressed the lipogenesis genes such as PPARc2, SREBP-1c, LPL, FAS and DGAT1. However, ginseng or ginsenosides, PD (Rb1) and PT (Re), showed different or contradictory results. Water and ethanol extraction of ginseng showed contradictory effects on the secretion of inflammatory cytokines, wheras IL-6 was repressed by ethanol extracts and TNF-${\alpha}$ repressed by Re in vitro. Based on the literature, further studies on anti-obese mechanisms of ginseng, such as the inflammation-related obesity or cross signals between the adipocytes and the environments, are needed, instead of more studies on its hypolipidemic and hypoglycemic effects.

• Genomics & Informatics
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• v.3 no.3
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• pp.86-93
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• 2005

Human brain EST data provide important clues for our understanding of the molecular biology associated with the function of the normal brain and the molecular pathophysiology with brain disorders. To systematically and efficiently study the function and disorders of the human brain, 45,773 human brain ESTs were collected from 27 human brain cDNA libraries, which were constructed from normal brains and brain disorders such as brain tumors, Parkinson's disease (PO) and epilepsy. An analysis of 45,773 human brain ESTs using our EST analysis pipeline resulted in 38,396 high-quality ESTs and 35,906 ESTs, which were coalesced into 8,246 unique gene clusters, showing a significant similarity to known genes in the human RefSeq, human mRNAs and UniGene database. In addition, among 8,246 gene clusters, 4,287 genes ($52\%$) were found to contain full-length cONA clones. To facilitate the extraction of useful information in collected these human brain ESTs, we developed a user-friendly interface system, the Korea Brain Unigene Database (KBUD). The KBUD web interface allows access to our human brain data through three major search modes, the BioCarta pathway, keywords and BLAST searches. Each result when viewed in KBUD offers comprehensive information concerning the analyzed human brain ESTs provided by our data as well as data linked to various other publiC databases. The user-friendly developed KBUD, the first world-wide web interface for human brain EST data with ESTs of human brain disorders as well as normal brains, will be a helpful system for developing a better understanding of the underlying mechanisms of the normal brain well as brain disorders. The KBUD system is freely accessible at http://kugi.kribb.re.kr/KU/cgi -bin/brain. pI.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.171-187
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• 2019

The study aims to analyze the posts of depression-related Facebook groups to understand major topics discussed by group users. Specifically, the purpose of the study is to identify the topics and keywords of the posts to understand what users discuss about depression. Depression is a mental disorder that is somewhat sensitive in the online community, which is characterized by accessibility, openness and anonymity. The researchers have implemented a natural language-based data analysis framework that includes components ranging from Facebook data collection to the automated extraction of topics. Using the framework, we collected and analyzed 885 posts created in the past one year from the largest Facebook depression group. To derive more complete and accurate topics, we combined both automated and manual (e.g., stop words removal, topic size determination) methods. Results indicate that users discuss a variety of topics including depression in general, human relations, mood and feeling, depression symptoms, suicide, medical references, family and etc.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.91-98
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• 2019

The present study was performed to investigate the general research trends of dyslexia and learning disability to explore the centrality of related variables though analysis of keyword networks. Data were collected from ten years articles research information sharing service(RISS) which is provided by korea education and research information service(KERIS). The research subjects selected for the analysis were keyword cleansing work, extraction major keyword using KrKwic program and using NodeXL program to Visualize the center of connection between keyword. The results of this were as follows. First, totally 72 of keyword were extracted from keyword cleansing process and among those keyword. major keywords included learning disability, dyslexia, RTI. Second, analysis of the betweenness centrality of dyslexia and learing disabilities shows that learning disabilities are a key word that has been addressed in the study of dyslexia and learning disabilities in korea. The results of these studies suggest a method of analyzing trends in qualitative and qualitative analysis in relation to dyslexia and learning disorder.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.497-510
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• 2019

This study was conducted to identify the domains of the competencies of trauma nursing through a scoping review using the JBI(Joanna Briggs Institute) methodology. The keywords are trauma, $nurs^*$, $competenc^*$, $role^*$, attitude, and knowledge and skill. The review used information from six databases: CINAHL, Pubmed, ProQuest, Web of Science, Scopus, and ERIC. Inclusion and exclusion criteria were identified as strategies to use in this review. 8 studies were eligible for result extraction, as they listed domains of the competencies. These domains among studies were analyzed based on Trauma Care System and Lenburg's COPA(Competency Outcomes and Performance Assessment) model. Domains in 'Prehospital care & transport', 'Hospital care' and 'Rehabilitation' of Trauma Care System were present, but no domain in 'Injury prevention' was.

• Safety and Health at Work
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• v.13 no.4
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• pp.379-386
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• 2022

Emergency medical services (EMS) personnel are at high risk for adverse mental health outcomes during disease outbreaks. To support the development of evidence-informed mitigation strategies, we conducted a scoping review to identify the extent of research pertaining to EMS personnel's mental health during disease outbreaks and summarized key factors associated with mental health outcomes. We systematically searched three databases for articles containing keywords within three concepts: EMS personnel, disease outbreaks, and mental health. We screened and retained original peer-reviewed articles that discussed, in English, EMS personnel's mental health during disease outbreaks. Where inferential statistics were reported, the associations between individual and work-related factors and mental health outcomes were synthesized. Twenty-five articles were eligible for data extraction. Our findings suggest that many of the contributing factors for adverse mental health outcomes are related to inadequacies in fulfilling EMS personnel's basic safety and informational needs. In preparation for future disease outbreaks, resources should be prioritized toward ensuring adequate provisions of personal protective equipment and infection prevention and control training. This scoping review serves as a launching pad for further research and intervention development.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1571-1576
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• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• Journal of Korean Academy of Rural Health Nursing
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• v.18 no.2
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• pp.113-124
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• 2023

Purpose: The number of households vulnerable to isolation increases rapidly as social ties decrease, raising concerns about the associated increase in lonely deaths. This study aimed to identify issues related to lonely deaths by analyzing South Korean news articles; and to provide evidence for their use in preventing and managing lonely deaths via community nursing. Methods: This exploratory study analyzed the structure and trends of meaning of lonely deaths by identifying the association between keywords in news articles and lonely deaths. In this study, we searched for all news articles on lonely deaths, covering the period from January 1, 2010, to May 31, 2023. Data preprocessing and purification were conducted, followed by top-keyword extraction, keyword network analysis and topic modeling. The retrieved articles were analyzed using R and Python software. Results: Four main topics were identified: "discovering and responding to lonely death cases", "lonely deaths ending in lonely funerals", "supportive policies to prevent lonely deaths among of older adults", and "local government activities to prevent lonely deaths and support vulnerable populations." Conclusion: Based on these findings, it can be concluded that lonely death is a complex social phenomenon that can be prevented if society shows concern and care. Education related to lonely deaths should be included in nursing curricula for concrete action plans and professional development.