• 한국산학기술학회논문지
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• 제19권10호
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• pp.176-182
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• 2018

본 연구는 건설 안전사고에 대한 트랜드 분석을 위해 LDA(Latent Dirichlet Allocation) 기반의 토픽모델링(Topic Modeling)을 제시하여 분석하고자 한다. 특히, 건설산업의 안전사고를 예방하기 위해 제시되고 있는 기존의 다양한 정형데이터 분석에서 벗어난 비정형 데이터 분석 기반의 토픽 모델링을 통해 건설 안전사고 주요 핵심 키워드의 흐름에 대해 파악이 가능하다. 본 방법론을 적용하기 위해 540개의 건설 안전사고 관련 뉴스데이터를 수집하였다. 이를 기반으로, 10가지 토픽과 각 토픽 내의 10가지 키워드를 통해 주요 이슈를 도출하였고 각 토픽에 대한 2017년 1월부터 2018년 2월까지의 뉴스 데이터를 월별 시계열 분석을 통해 향후 토픽에 관한 이슈를 예측한다. 본 연구를 바탕으로 향후 건설 안전사고의 다양한 이슈를 선제적으로 예측하고 이를 기반으로 건설 안전사고 정책과 연구에 좋은 방향을 제시할 것으로 판단한다.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• 디지털콘텐츠학회 논문지
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• 제19권5호
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• pp.981-993
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• 2018

스마트 헬스케어는 ICT 분야와 의료서비스 분야가 융 복합 된 분야로 다양한 분야에서 학제 간 융 복합 연구가 활발히 이루어지고 있다. 본 연구는 토픽모델링(Topic Modeling)과 에고 네트워크 분석(Ego Network Analysis)을 활용하여 스마트 헬스케어 연구동향을 살피는데 그 목적이 있다. 이를 위해 2001년부터 2018년 4월까지 Scopus에 게재된 2,690편을 대상으로 텍스트 분석, 각 기간별 빈도분석, 토픽모델링, 워드 클라우드, 에고 네트워크 분석을 수행하였다. 토픽 모델링 분석 결과 8개의 주요 연구토픽이 도출되었다. 8개 주요 연구토픽은 "AI in healthcare", " Smart hospital", "Healthcare platform", " blockchain in healthcare", "Smart health data", "Mobile healthcare", "Wellness care", "Cognitive healthcare" 순으로 나타났다. 토픽모델링 결과를 보다 심도 있게 살펴보기 위해 연구토픽별 에고 네트워크 분석을 하였다. 이를 통해 스마트 헬스케어 연구동향을 파악하고, 향후 연구의 방향성을 수립하는데 시사점을 제시하고자 한다.

• 한국IT서비스학회지
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• 제15권2호
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• pp.81-92
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• 2016

Numerous people share their TV experience with other viewers on social media such as personal blogs and Twitter. It means that broadcast media, especially TV, affects the responses on social media. Moreover, the responses affect broadcast media ratings back. Social TV tried to use the relationship in marketing activities such as advertisement by analyzing the TV related social behavior. However, most of them used just the quantities of social media responses. This study analyzes the subjects of the responses on social media about specific TV dramas through topic modeling, and the relationship between the changes of popular topics and viewer ratings of the drama over specified periods. Five representative Korean dramas of 2014 were selected and Blog contents including viewer ratings about the dramas were collected from naver.com which is the representative portal in South Korea. The proposed analysis framework consists of three steps which are Blogs crawling, topic modeling, and topic trend analysis. We found some implications from the results of the topic trend analysis. Firstly, there were specific topics on dramas in social media. Secondly, the topics had some meaningful relationships with viewer ratings. Lastly, there were differences between the topics of dramas with higher viewer ratings and those with lower viewer ratings.

• Journal of Korean Biological Nursing Science
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• 제23권3호
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• pp.170-179
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Diabetes mellitus and mobile applications' field of research for better understanding research trends in the past 20 years. Methods: This study was a text-mining and topic modeling study including four steps such as 'collecting abstracts', 'extracting and cleaning semantic morphemes', 'building a co-occurrence matrix', and 'analyzing network features and clustering topic groups'. Results: A total of 789 papers published between 2002 and 2021 were found in databases (Springer). Among them, 435 words were extracted from 118 articles selected according to the conditions: 'analyzed by text network analysis and topic modeling'. The core keywords were 'self-management', 'intervention', 'health', 'support', 'technique' and 'system'. Through the topic modeling analysis, four themes were derived: 'intervention', 'blood glucose level control', 'self-management' and 'mobile health'. The main topic of this study was 'self-management'. Conclusion: While more recent work has investigated mobile applications, the highest feature was related to self-management in the diabetes care and prevention. Nursing interventions utilizing mobile application are expected to not only effective and powerful glycemic control and self-management tools, but can be also used for patient-driven lifestyle modification.

• Journal of Information Science Theory and Practice
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• 제6권4호
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• pp.39-53
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• 2018

While the body of research on metadata has grown substantially, there has been a lack of systematic analysis of the field of metadata. In this study, we attempt to fill this gap by examining metadata literature spanning the past 20 years. With the combination of a text mining technique, topic modeling, and network analysis, we analyzed 2,713 scholarly papers on metadata published between 1995 and 2014 and identified main topics and trends in metadata research. As the result of topic modeling, 20 topics were discovered and, among those, the most prominent topics were reviewed in detail. In addition, the changes over time in the topic composition, in terms of both the relative topic proportions and the structure of topic networks, were traced to find past and emerging trends in research. The results show that a number of core themes in metadata research have been established over the past decades and the field has advanced, embracing and responding to the dynamic changes in information environments as well as new developments in the professional field.

• 한국정보통신학회논문지
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• 제26권11호
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• pp.1751-1754
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• 2022

The news articles collected with keyword "non face-to-face" were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-face-to-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing.

• 한국프랜차이즈경영연구
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• 제12권4호
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• pp.13-23
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• 2021

Purpose: This study aimed to derive clues for the franchise industry to overcome difficulties such as various legal regulations and social responsibility demands and to continuously develop by analyzing the research trends related to franchises published in Korea. Research design, data and methodology: As a result of searching for 'franchise' in ScienceON, abstracts were collected from papers published in domestic academic journals from 1994 to June 2021. Keywords were extracted from the abstracts of 1,110 valid papers, and after preprocessing, keyword analysis, TF-IDF analysis, and topic modeling using LDA algorithm, along with trend analysis of the top 20 words in TF-IDF by year group was carried out using the R-package. Results: As a result of keyword analysis, it was found that businesses and brands were the subjects of research related to franchises, and interest in service and satisfaction was considerable, and food and coffee were prominently studied as industries. As a result of TF-IDF calculation, it was found that brand, satisfaction, franchisor, and coffee were ranked at the top. As a result of LDA-based topic modeling, a total of 12 topics including "growth strategy" were derived and visualized with LDAvis. On the other hand, the areas of Topic 1 (growth strategy) and Topic 9 (organizational culture), Topic 4 (consumption experience) and Topic 6 (contribution and loyalty), Topic 7 (brand image) and Topic 10 (commercial area) overlap significantly. Finally, the trend analysis results for the top 20 keywords with high TF-IDF showed that 10 keywords such as quality, brand, food, and trust would be more utilized overall. Conclusions: Through the results of this study, the direction of interest in the franchise industry was confirmed, and it was found that it was necessary to find a clue for continuous growth through research in more diverse fields. And it was also considered an important finding to suggest a technique that can supplement the problems of topic trend analysis. Therefore, the results of this study show that researchers will gain significant insights from the perspectives related to the selection of research topics, and practitioners from the perspectives related to future franchise changes.

• 정보관리학회지
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• 제22권3호
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• pp.289-306
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• 2005

FRBR 모델에서는 서지 요소와 관계를 중심으로 ER 모델링 방식을 제공하고 있지만, 단지 구조적 프레임워크로서 FRBR 모델을 효율적으로 구현할 수 있는 도구가 필요하다. 본 연구에서는 Topic Maps를 이용하여 FRBR 모델을 구현하는 방법을 제시한다. Topic Maps 기반의 FRBR 모델 구현의 유효성을 실증적으로 보이기 위하여, 명성황후라는 주제와 관련된 MARC 데이터를 추출하여 FRBR 모델을 설계하였고, Topic Maps를 이용하여 이를 구현하였다. 연구 결과, FRBR의 entity-relation과 Topic Maps의 topic-association이 개념적으로 동일하기 때문에 FRBR 모델 개발의 적합함을 알 수 있었다. FRBR 구조는 Topic Maps 패러다임과 그대로 일치하기 때문에 FRBR 모델은 Topic Maps로 구현함이 바람직하다.

• 정보관리학회지
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• 제32권1호
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• pp.153-169
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• 2015

이 연구에서는 토픽 모델링 결과 해석의 용이성을 위하여, 동적 인용 네트워크를 활용하여 LDA 기반 토픽 모델링의 토픽 수를 설정하고 중복 배치된 주요 키워드를 자아 중심 네트워크 분석을 통해 재배치하여 제시하는 방법을 제안하였다. 'White LED' 두 분야의 논문 데이터를 이용하여 분석한 결과, 동적 인용 네트워크 분석을 통해 형성된 분석대상 문헌집단에 혼잡도에 따른 토픽수를 사용하고 중복 분류된 토픽 내 주요 키워드를 자아중심 네트워크 분석 기법을 적용하여 재배치한 결과가 토픽 간의 중복도가 가장 낮은 것으로 나타났다. 따라서 동적 인용 네트워크 및 자아 중심 네트워크 분석을 적용함으로써 토픽모델링에 의한 분석 결과를 보완하는 다면적인 연구 동향 분석이 가능할 것으로 보인다.