• 실내환경 및 냄새 학회지
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• 제17권4호
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• pp.322-329
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• 2018

The objective of this study is to identify the research trend in the field of indoor environment in Korea. We collected 419 papers published in the Journal of the Korean Society for indoor environment between 2004 and 2018, and attempted to produce datasets using a topic modeling technique, Latent Dirichlet Allocation(LDA). The result of topic modeling showed that 8 topics ("VOCs investigation", "Subway environment", "Building thermal environment", "School health", "Building particulate matter", "Asbestos risk", "Radon risk", "Air cleaner and treatment") could be extracted using Gibbs sampling method. In terms of topic trends, investigation of volatile organic compounds, subway environment, school health, and building particulate matter showed a decreasing tendency, while the building thermal environment, asbestos risk, radon risk, air cleaners, and air treatment showed an increasing tendency. The results of this topic modeling could help us to understand current trends related indoor environment, and provide valuable information in developing future research and policy frameworks.

• 한국응급구조학회지
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• 제26권2호
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• pp.19-35
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• 2022

Purpose: This study aimed to identify research topics in the emergency medical technician (EMT) field and examine research trends. Methods: In this study, 261 research papers published between January 2000 and May 2022 were collected, and EMT research topics and trends were analyzed using topic modeling techniques. This study used a text mining technique and was conducted using data collection flow, keyword preprocessing, and analysis. Keyword preprocessing and data analysis were done with the RStudio Version 4.0.0 program. Results: Keywords were derived through topic modeling analysis, and eight topics were ultimately identified: patient treatment, various roles, the performance of duties, cardiopulmonary resuscitation, triage systems, job stress, disaster management, and education programs. Conclusion: Based on the research results, it is believed that a study on the development and application of education programs that can successfully increase the emergency care capabilities of EMTs is needed.

• Asian Journal of Innovation and Policy
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• 제12권3호
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• pp.345-362
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• 2023

As the widespread adoption and proliferation of electric vehicles continue, the secondary battery market is experiencing rapid growth. However, lithium-ion batteries, which constitute a majority of secondary batteries, present high risks of fire and explosion. Solid-state batteries are thus garnering attention as the next-generation batteries since they eliminate fire hazards and significantly reduce the risk of explosions. Against this background, the study aimed to analyze research trends and provide insights by examining 2,927 domestic papers related to solid-state batteries over the past decade (2013-2022). Specifically, we used topic modeling to extract major keywords associated with solid-state batteries research and to explore the network characteristics across major topics. The changes in research on solid-state batteries were analyzed in-depth by calculating topic dominance by year. The findings provide an overview of the emerging trends in domestic solid-state battery research, and might serve as a valuable reference in shaping long-term research directions.

• 산업경영시스템학회지
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• 제40권4호
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• pp.46-54
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• 2017

'Consulting', which is the main research topic of the knowledge service industry, is a field of study that is essential for the growth and development of companies and proliferation to specialized fields. However, it is difficult to grasp the current status of international research related to consulting, mainly on which topics are being studied, and what are the latest research topics. The purpose of this study is to analyze the research trends of academic research related to 'consulting' by applying quantitative analysis such as topic modeling and statistic analysis. In this study, we collected statistical data related to consulting in the Scopus DB of Elsevier, which is a representative academic database, and conducted a quantitative analysis on 15,888 documents. We scientifically analyzed the research trends related to consulting based on the bibliographic data of academic research published all over the world. Specifically, the trends of the number of articles published in the major countries including Korea, the author key word trend, and the research topic trend were compared by country and year. This study is significant in that it presents the result of quantitative analysis based on bibliographic data in the academic DB in order to scientifically analyze the trend of academic research related to consulting. Especially, it is meaningful that the traditional frequency-based quantitative bibliographic analysis method and the text mining (topic modeling) technique are used together and analyzed. The results of this study can be used as a tool to guide the direction of research in consulting field. It is expected that it will help to predict the promising field, changes and trends of consulting industry related research through the trend analysis.

• 한국산학기술학회논문지
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• 제22권3호
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• pp.661-670
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• 2021

연구동향은 시기별 연구주제에 대한 중요도 판단과 부족한 연구 분야를 파악하고 신규 분야를 발굴하는데 유용하게 활용될 수 있다. 본 연구에서는 인구집중과 도시화로 인해 다양한 문제가 발생하고 있는 도시공간을 대상으로 한 논문들을 대상으로 시기별 연구동향을 분석하였다. 이를 위해 2002년부터 2019년 사이에 게재된 한국학술지인용색인(KCI)에 등재된 논문의 초록을 대상으로 데이터마이닝 기법 중 하나인 토픽모델링 분석을 수행하였다. 토픽모델링은 전체 내용에서 일정한 패턴을 발견해낼 수 있는 알고리즘 기반의 텍스트마이닝 기법으로 방대한 문헌에서 주제를 찾아내고 군집하는데 용이하다. 본 연구에서는 키워드 빈도, 연도별 경향, 토픽 도출, 토픽별 군집, 토픽유형별 경향에 대한 분석을 실시하였다. 그 결과 먼저 도시재생 분야연구가 지속적으로 증가되고 있고 앞으로도 세부 주제가 확대될 수 있는 분야로 분석되었다. 그리고 도시재생 주제는 이제 정규 연구분야로 자리 잡고 있는 것으로 파악되었다. 반면, 개발/성장과 에너지/환경과 같은 주제는 정체기에 들어간 것으로 분석되었다. 본 연구는 국내 전체 도시분야 연구를 대상으로 데이터마이닝 기법인 토픽모델링을 이용하여 키워드 간 연관성과 경향을 함께 분석하였다는 데 의의가 있다.

• 전기학회논문지
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• 제66권4호
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• pp.613-620
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• 2017

The power grid has been changed to a smart grid system to satisfy the growing need for power grid complexity, demand, reliability, security, and efficiency with a combination of existing power and ICT technology. This study analyzes the research trends in smart grid technology in the period since the introduction of the smart grid system and compares it with industrial trends to grasp the progress and characteristics of Smart Grid technology and look for ways to innovate the technology. To do this, we analyze the research trends using dynamic topic modeling, which is capable of time-series research topic analysis. Next, we compare the results of research trends with industrial trends analyzed by Gartner's experts to demonstrate that smart grid research is evolving to the level of industrialization. The results of this study are quantitative analysis through data mining, and it is expected that it will be used in many fields such as companies that want to participate in industry and government agencies that need to establish policies by showing more objective analysis results.

• 무역학회지
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• 제45권3호
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.329-342
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• 2020

In recent years, emotional text classification is one of the essential research contents in the field of natural language processing. It has been widely used in the sentiment analysis of commodities like hotels, and other commentary corpus. This paper proposes an improved W-LDA (weighted latent Dirichlet allocation) topic model to improve the shortcomings of traditional LDA topic models. In the process of the topic of word sampling and its word distribution expectation calculation of the Gibbs of the W-LDA topic model. An average weighted value is adopted to avoid topic-related words from being submerged by high-frequency words, to improve the distinction of the topic. It further integrates the highest classification of the algorithm of support vector machine based on the extracted high-quality document-topic distribution and topic-word vectors. Finally, an efficient integration method is constructed for the analysis and extraction of emotional words, topic distribution calculations, and sentiment classification. Through tests on real teaching evaluation data and test set of public comment set, the results show that the method proposed in the paper has distinct advantages compared with other two typical algorithms in terms of subject differentiation, classification precision, and F1-measure.

• 융합경영연구
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• 제9권1호
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• 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.