• 품질경영학회지
• /
• 제50권1호
• /
• pp.77-89
• /
• 2022

Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.

• 예술인문사회 융합 멀티미디어 논문지
• /
• 제6권5호
• /
• pp.409-419
• /
• 2016

일반적으로 지역학 연구를 수행하는데 있어 연구 동향을 파악하는 것은 매우 중요하다. 그러나 지역학의 연구 분야는 매우 다양하며, 모든 지역학 연구 분야에 대한 연구가 동시에 진행되는 것은 매우 어렵다. 이로 인해 지역학연구는 시대에 따라 연구 분야 및 연구 동향이 변화 하였다. 이와 함께 지역학의 연구 동향을 이해하려는 관심이 꾸준히 증가되고 있다. 본 논문에서는 국내의 지중해지역 연구를 대상으로 동시 출현 키워드를 기반으로 연구 동향을 분석한다. 이를 위하여 국내 지중해지역 연구의 대표 학술지인 『지중해지역연구』에 게재된 논문들을 대상으로 논문 유형 분석 및 키워드를 추출하여 정제 과정을 거쳐 동시 출현 키워드를 생성하였다. 세부적으로 논문의 유형 분석을 통해 기본적인 동향 분석을 수행하였고, 논문의 동시 출현 키워드를 이용하여 단순 정량 분석보다 심층적인 분석을 수행하고, 동시출현 키워드를 통해 생성된 네트워크 그래프 형태의 시각화를 통해 분석을 수행한다.

• 대한간호학회지
• /
• 제53권1호
• /
• pp.55-68
• /
• 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.

• 복식문화연구
• /
• 제31권1호
• /
• pp.34-52
• /
• 2023

In this study, text mining was conducted on the product names of skirts, pants, shirts/blouses, and dresses to analyze the characteristics of keywords appearing in online shopping product names. As a result of frequency analysis, the number of keywords that appeared 0.5% or more for each item was around 30, and the number of keywords that appeared 0.1% or more was around 150. The cumulative distribution rate of 150 terms was around 80%. Accordingly, information on 150 key terms was analyzed, from which item, clothing composition, and material information were the found to be the most important types of information (ranking in the top five of all items). In addition, fit and style information for skirts and pants and length information for skirts and dresses were also considered important information. Keywords representing clothing composition information were: banding, high waist, and split for skirts and pants; and V-neck, tie, long sleeves, and puff for shirts/blouses and dresses. It was possible to identify the current design characteristics preferred by consumers from this information. However, there were also problems with terminology that hindered the connection between sellers and consumers. The most common problems were the use of various terms with the same meaning and irregular use of Korean and English terms. However, as a result of using co-appearance frequency analysis, it can be interpreted that there is little intention for product exposure, so it is recommended to avoid it.

• Journal of Nutrition and Health
• /
• 제56권3호
• /
• pp.330-347
• /
• 2023

Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

• 산업경영시스템학회지
• /
• 제46권4호
• /
• pp.246-262
• /
• 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.

• 정보관리학회지
• /
• 제40권4호
• /
• pp.351-373
• /
• 2023

이 연구는 국내 기록관리표준의 연구동향을 분석한 것으로 이를 위해 기록관리표준 관련 논문의 표제-주제어-초록의 키워드를 추출하여 상위빈도 키워드의 분석과 키워드 네트워크 분석을 수행하였다. 분석 대상 기간은 2000년부터 현재까지이며 RISS와 ScienceON 등의 국내 학술논문 검색사이트에서 총 212편의 논문을 수집하여 연구를 수행하였다. 분석 결과 2000~2010년까지는 아카이브 설계를 위한 OAIS의 연구, OAIS를 통한 디지털 기록 보존연구 ISO 표준의 분석 연구 등이 주로 진행되었고, 2011년 이후부터 지금까지는 기록경영인증, ISAD(G)의 RiC 전환 등의 연구가 진행되었음을 알 수 있었다. 이 연구는 기록관리표준 연구의 국내 연구동향을 분석함으로써 연구 흐름을 파악하는 기초자료로 활용되며, 기존 기록관리표준을 연구할 때 참고자료로 역할을 할 것으로 기대한다.

• 무역학회지
• /
• 제45권5호
• /
• pp.37-53
• /
• 2020

This study aims to provide the implications of research development by comparing domestic and international studies conducted on the subject of FTA. To this end, among the papers written during the period from 2000 to July 23, 2020, papers whose title is searched by FTA (Free Trade Agreement) were selected as research data. In the case of domestic research, 1,944 searches from the Korean Citation Index (KCI) and 970 from the Web of Science and SCOPUS were selected for international research, and the research trend was analyzed through keywords and abstracts. Frequency analysis and word embedding (Word2vec) were used to analyze the data and visualized using t-SNE and Scattertext. The results of the analysis are as follows. First, in the top 30 keywords of domestic and international research, 16 out of 30 were found to be the same. In domestic research, many studies have been conducted to analyze the outcomes or expected effects of countries that have concluded or discussed FTAs with Korea, on the other hand there are diverse range of study subjects in international research. Second, in the word embedding analysis, t-SNE was used to visually represent the research connection of the top 60 keywords. Finally, Scattertext was used to visually indicate which keywords were frequently used in studies from 2000 to 2010, and from 2011 to 2020. This study is the first to draw implications for academic development through abstract and keyword analysis by applying various text mining approaches to the FTA related research papers. Further in-depth research is needed, including collecting a variety of FTA related text data, comparing and analyzing FTA studies in different countries.

• 한국간호교육학회지
• /
• 제30권2호
• /
• pp.149-159
• /
• 2024

Purpose: The aim of this study was to identify core keywords and topic groups in the "adolescent pregnancy" field of research for a better understanding of research trends in the past 10 years. Methods: Topics related to adolescent pregnancy were extracted from 3,819 articles that were published in journals between January 2013 and July 2023. Abstracts were retrieved from five databases (MEDLINE, CINAHL, Embase, RISS, and KISS). Keywords were extracted from the abstracts and cleaned using semantic morphemes. Text network analysis and topic modeling were performed using NetMiner 4.3.3. Results: The most important keywords were "health," "woman," "risk," "group," "girl," "school," "service," "family," "program," and "contraception." Five topic groups were identified through topic modeling. Through the topic modeling analysis, five themes were derived: "health service," "community program for school girls," "risks for adult women," "relationship risks," and "sexual contraceptive knowledge." Conclusion: This study utilized text network analysis and topic modeling to analyze keywords from abstracts of research conducted over the past decade on adolescent pregnancy. Given that adolescent pregnancy leads to physical, mental, social, and economic issues, it is imperative to provide integrated intervention programs, including prenatal/postnatal care, psychological services, proper contraception methods, and sex education, through school and community partnerships, as well as related research studies. Nurses can play a vital role by actively engaging in prevention efforts and directly supporting and educating socially disadvantaged adolescent mothers, which could significantly contribute to improving their quality of life.

• 한국비블리아학회지
• /
• 제35권2호
• /
• pp.85-108
• /
• 2024

본 연구는 최근 15년간의 연구데이터 관련 연구 현황을 파악하기 위하여 2010년부터 2024년까지의 연구데이터 학술논문의 연구 동향을 분석하고자 하였다. 목적을 달성하고자 Scopus DB에 게재된 학술논문 14,921편을 대상으로 키워드 빈도 분석 및 네트워크 중심성 분석을 수행하였다. 학술지 게재 시기에 따라 1기(2010-2014년), 2기(2015-2019년), 3기(2020-2024년)로 구분하여 UCINET을 활용한 키워드 네트워크 분석을 수행한 결과, 시기에 상관없이 연구되는 주요 키워드와 기간별로 주목받는 키워드, 시간이 지나면서 관심이 줄어드는 키워드를 도출하였다. 최근 15년간 연구데이터 관련 연구가 가장 활발히 이루어진 주제는 데이터 공유인 것으로 확인되었으며, 연결 중심성이 높은 키워드들이 대부분 매개 중심성 또한 높은 것으로 나타났다. 본 연구의 결과는 향후 국내 연구데이터 분야의 연구 방향성을 제시하는 기초자료로서 활용될 수 있을 것으로 판단된다.