• 치위생과학회지
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• 제23권4호
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• pp.302-311
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• 2023

Background: Oral health is an important element of well aging. And oral health also affects overall health, mental health, and quality of life. In this study, we sought to identify oral health influencing factors and research trends for well-aging through text analysis of research on well-aging and oral health over the past 12 years. Methods: The research data was analyzed based on English literature published in PubMed from 2012 to 2023. Aging well and oral health were used as search terms, and 115 final papers were selected. Network text analysis included keyword frequency analysis, centrality analysis, and cohesion structure analysis using the Net-Miner 4.0 program. Results: Excluding general characteristics, the most frequent keywords in 115 articles, 520 keywords (Mesh terms) were psychology, dental prosthesis and Alzheimer's disease, Dental caries, cognition, cognitive dysfunction, and bacteria. Research keywords with high degree centrality were Dental caries (0.864), Quality of life (0.833), Tooth loss (0.818), Health status (0.727), and Life expectancy (0.712). As a result of community analysis, it consisted of 4 groups. Group 1 consisted of chewing and nutrition, Group 2 consisted oral diseases, systemic diseases and management, Group 3 consisted oral health and mental health, Group 4 consisted oral frailty symptoms and quality of life. Conclusion: In an aging society, oral dysfunction affects mental health and quality of life. Preventing oral diseases for well-aging can have a positive impact on mental health and quality of life. Therefore, efforts are needed to prevent oral frailty in a super-aging society by developing and educating systematic oral care programs for each life cycle.

• International Journal of Contents
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• 제10권1호
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• pp.29-35
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• 2014

This paper proposes an approach for visualizing research trends using theme maps and extra information. The proposed algorithm includes the following steps. First, text mining is used to construct a vector space of keywords. Second, correspondence analysis is employed to reduce high-dimensionality and to express relationships between documents and keywords. Third, kernel density estimation is applied in order to generate three-dimensional data that can show the concentration of the set of documents. Fourth, a cartographical concept is adapted for visualizing research trends. Finally, relative vitalization information is provided for more accurate research trend analysis. The algorithm of the proposed approach is tested using papers about Traditional Korean Medicine.

• Asian Journal of Innovation and Policy
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• 제9권3호
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• pp.339-359
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• 2020

The policy change in the Data 3 Act is one of the issues that should be noted at a time when non-face-to-face business strategies become important after COVID-19. The Data 3 Act was implemented in South Korea on August 5, 2020, calling 'Big Data 3 Act' and 'Data Economy 3 Act,' and so personal information that was not able to identify a particular individual could be utilized without the consent of the individual. With the implementation of the Data 3 Act, it is possible to establish a fair economic ecosystem by ensuring fair access to data and various uses. In this paper, the law on the protection of personal information, which is the core of the Data 3 Act, was compared around Korea, the European Union and the United States, and the implications were derived through network analysis of keywords.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.676-691
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• 2022

An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.149-163
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• 2023

In this study we analyze research trends on machine learning healthcare based on papers from the United States, the United Kingdom, and Korea. In Elsevier's Scopus, we collected 3425 papers related to machine learning healthcare published from 2018 to 2022. Keyword frequency and centrality analysis were conducted using the abstracts of the collected papers. We identified keywords with high frequency of appearance by calculating keyword frequency and found central research keywords through the centrality analysis by country. Through the analysis results, research related to machine learning, deep learning, healthcare, and the covid virus was conducted as the most central and highly mediating research in each country. As the implication, studies related to electronic health information-based treatment, natural language processing, and privacy in Korea have lower degree centrality and betweenness centrality than those of the United States and the United Kingdom. Thus, various convergence research applied with machine learning is needed for these fields.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.13-27
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• 2023

In this study, we tried to identify research trends on artificial intelligence security focusing on the United States, United Kingdom, and South Korea. In Elsevier's Scopus We collected 4,983 papers related to artificial intelligence security published from 2018 to 2022 and by using the abstracts of the collected papers, Keyword frequency and centrality analysis were conducted. By calculating keyword frequency, keywords with high frequency of appearance were identified and through the centrality analysis, central research keywords were identified by country. Through the analysis results, research related to artificial intelligence, machine learning, Internet of Things, and cybersecurity in each country was conducted as the most central and highly mediating research. The implication for Korea is that research related to cybersecurity, privacy, and anomaly detection has lower centralities compared to the United States and research related to big data has lower centralities compared to United Kingdom. Therefore, various researches that intensively apply artificial intelligence technology to these fields are needed.

• 디지털산업정보학회논문지
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• 제20권1호
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• pp.1-15
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• 2024

This study aims to analyze research trends in blockchain-based Internet of Things focusing on the US, UK, and Korea. In Elsevier's Scopus, we collected 2,174 papers about blockchain-based Internet of Things published in from 2018 to 2023. Keyword frequency and centrality analysis were conducted on the abstracts of the collected papers. Through the obtained keyword frequencies, we tried to identify keywords with high frequency of occurrence and through centrality analysis, we tried to identify central research keywords for each country. As a result of the centrality analysis, research on blockchain, smart contracts, Internet of Things, security and personal information protection was conducted as the most central research in each country. The implication for Korea is that cybersecurity, authentication research appears to have been conducted with a lower centrality compared to the United States and the United Kingdom. Thus, it seems that intensive research related to cybersecurity and authentication is needed.

• 지능정보연구
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• 제25권3호
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• pp.19-41
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• 2019

텍스트 데이터에 대한 다양한 분석을 위해 최근 비정형 텍스트 데이터를 구조화하는 방안에 대한 연구가 활발하게 이루어지고 있다. doc2Vec으로 대표되는 기존 문서 임베딩 방법은 문서가 포함한 모든 단어를 사용하여 벡터를 만들기 때문에, 문서 벡터가 핵심 단어뿐 아니라 주변 단어의 영향도 함께 받는다는 한계가 있다. 또한 기존 문서 임베딩 방법은 하나의 문서가 하나의 벡터로 표현되기 때문에, 다양한 주제를 복합적으로 갖는 복합 문서를 정확하게 사상하기 어렵다는 한계를 갖는다. 본 논문에서는 기존의 문서 임베딩이 갖는 이러한 두 가지 한계를 극복하기 위해 다중 벡터 문서 임베딩 방법론을 새롭게 제안한다. 구체적으로 제안 방법론은 전체 단어가 아닌 핵심 단어만 이용하여 문서를 벡터화하고, 문서가 포함하는 다양한 주제를 분해하여 하나의 문서를 여러 벡터의 집합으로 표현한다. KISS에서 수집한 총 3,147개의 논문에 대한 실험을 통해 복합 문서를 단일 벡터로 표현하는 경우의 벡터 왜곡 현상을 확인하였으며, 복합 문서를 의미적으로 분해하여 다중 벡터로 나타내는 제안 방법론에 의해 이러한 왜곡 현상을 보정하고 각 문서를 더욱 정확하게 임베딩할 수 있음을 확인하였다.

• 한국응용과학기술학회지
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• 제40권5호
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• pp.1072-1080
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• 2023

본 연구의 목적은 스포츠분야 '플로깅'에 관한 주요 키워드와 사회현상을 분석하고 중요 정보를 도출하는 것이다. 이러한 목적을 달성하기 위해 한국언론진흥재단에서 제공하는 뉴스분석시스템 빅카인즈(BIG Kinds)를 활용하여 분석하였다. 분석기간은 2018년부터 2022년이며, 수집된 뉴스 5,148건 중 스포츠분야에 해당하는 42건을 최종적으로 사용하여 분석하였다. 분석방법은 빈도분석, 관계도분석, 연관어분석을 실시하였으며 결과는 다음과 같다. 첫째, 스포츠분야 '플로깅' 관련 빈도 분석 결과, '제주', '선수들', '구국제마라톤대회', 'SSG', '이봉주 선수' 등의 키워드를 확인할 수 있었다. 둘째, 스포츠분야 '플로깅' 관련 관계도분석 결과, '코로나19', '국가대표', '엘리트', '마스터즈', '코로나' 등의 키워드를 확인할 수 있었다. 셋째, 스포츠분야 '플로깅' 관련 연관어분석 결과, '합성어', '봉사활동', '마스터즈 비대면', '제주', '선수들' 등의 키워드를 확인할 수 있었다. 국내 프로스포츠 분야의 플로깅은 국제 스포츠기구의 탄소중립실천을 위한 환경 활동 참여와 프로구단의 홍보를 목적으로 적극 활용하는 것으로 나타났다.

• 한국콘텐츠학회논문지
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• 제20권2호
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• pp.162-171
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• 2020

본 연구는 Textom 빅데이터를 바탕으로 유아 재난안전교육에 대한 사회 전반의 인식을 살펴보고 유아 재난안전교육의 방향을 탐색하는데 목적이 있다. 이를 위해 2014년부터 2017년 까지 포털 웹사이트에서 '유아+재난+안전교육'을 키워드로 온라인 텍스터 데이터를 수집하고 분석하였다. 수집된 원자료는 1차와 2차 데이터 정제과정을 거쳤으며, 빈도분석 결과를 바탕으로 주요단어 50개를 선정하였으며, 선정된 키워드는 매트릭스 데이터로 변환하여 네트워크 분석을 실시하였다. 연구결과 첫째, 유아 재난안전교육과 함께 가장 높은 빈도로 등장한 키워드로는 '교육'이었으며, 그 다음으로 '체험', '유치원', '예방', '학교' 순으로 나타났다. 둘째, 중심성 분석 결과, 연결중심성, 근접중심성, 매개중심성이 가장 높은 키워드 역시 '교육', '체험', '예방' 순으로 나타났다. 또한 '예방', '생활', '대피' 키워드는 빈도순위보다 연결중심성에서 높은 순위가 나타나 단어들 간의 연결정도가 높다고 할 수 있다. 이러한 결과들은 유아의 재난안전능력을 증진시키기 위해서는 유아기에 '교육'이 필요하며, 교육기관에서 '예방'과 '체험'을 통한 교육이 이루어져야 함을 시사한다.