• Journal of Digital Convergence
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• v.19 no.5
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• pp.31-39
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• 2021

This study collected 1,870 English news articles related to research and development of autonomous vehicles in order to identify various issues emerging in the research and development process of autonomous vehicles at home and abroad, and conducted topic modeling after data pre-processing. As a result of topic modeling, we extracted 20 topics, and we performed naming operations for topics and interpreted their meanings. A logical model for autonomous vehicle research and development projects was presented in response to the R&D process of input, activity, output, and outcome of derived topics. The analysis results of this study will be used as basic data to accurately determine the progress of domestic and foreign self-driving car research and development projects and prepare for the rapidly changing technology development.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.28-30
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• 2006

텍스트마이닝(text mining) 기법을 통해 생물학 문헌으로부터 단백질 이름과 그들 간의 상호 관계를 추출하는 시스템이 제안된 바 있다[1]. 이 시스템에서 단백질 이름을 추출하는 과정을 패턴 일치 오토마타(PMA: Pattern Matching Automata)라는 방법을 이용하여 좀 더 유연하고 높은 성능을 가지도록 개선할 수 있었다. 본 논문은 예제를 통해 PMA의 학습, 테스트 과정과 결과를 설명함으로써 단백질 이름 추출작업에서의 PMA의 가능성과 성능 향상을 위한 앞으로의 방안을 제시한다.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.195-197
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• 2023

본 연구는 재난에서 사용되고 있는 정보통신기술( ICT) 연구 국내논문 100개 및 해외논문 100개를 수집한 후, R 프로그램을 이용한 텍스트 마이닝 분석을 통해 국내 재난관리 네 가지 단계에 따른 ICT 기술 관련 연구 동향을 파악하고, 해외 재난관리의 ICT 기술 연구 동향을 분석 활용하여 우리나라 재난에서의 ICT 기술 활용방안을 새롭게 구성해 제언하는 것이다.

• Smart Media Journal
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• v.11 no.8
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• pp.9-20
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• 2022

With the rise of the metaverse industry worldwide, relevant national strategies and nurturing systems have been prepared in Korea. As the complexity of policies increases, the importance of establishing data-based policymkaing is growing, and studies diagnosing national R&D trends in the metaverse field are still lacking. Therefore, this paper collected NTIS national R&D information for 9,651 R&D projects promoted from 2002 to 2020. And this study looked at the current status and identified major topics based on the topic modeling, and considered time-series changes in the topics. Eleven major topics of R&D tasks in the metaverse field were derived, hot topics were service/content/platform development and medical/surgical fields of application fields, and cold topics were urban/environment/spatial information fields. Strategic R&D Management, metaverse-related laws, and institutional studies were proposed as policy directions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.640-649
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• 2018

This study analyzed the consumer awareness of cycling wear using web mining, one of the big data analysis methods. For this, the texts of postings and comments related to cycling wear from 2006 to 2017 at Naver cafe, 'people who commute by bicycle' were collected and analyzed using R packages. A total of 15,321 documents were used for data analysis. The keywords of cycling wear were extracted using a Korean morphological analyzer (KoNLP) and converted to TDM (Term Document Matrix) and co-occurrence matrix to calculate the frequency of the keywords. The most frequent keyword in cycling wear was 'tights', including the opinion that they feel embarrassed because they are too tight. When they purchase cycling wear, they appeared to consider 'price', 'size', and 'brand'. Recently 'low price' and 'cost effectiveness' have become more frequent since 2016 than before, which indicates that consumers tend to prefer practical products. Moreover, the findings showed that it is necessary to improve not only the design and wearability, but also the material functionality, such as sweat-absorbance and quick drying, and the function of pad. These showed similar results to previous studies using a questionnaire. Therefore, it is expected to be used as an objective indicator that can be reflected in product development by real-time analysis of the opinions and requirements of consumers using web mining.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.420-429
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• 2009

Patent text is a rich source for discovering technological trends. In order to automate such a discovery process, we attempt to identify phrases corresponding to the problem and its solution method which together form a technology. Problem and solution phrases are identified by a SVM classifier using features based on a combination of a language modeling approach and linguistic clues. Based on the occurrence statistics of the phrases, we identify the time span of each problem and solution and finally generate a trend. Based on our experiment, we show that the proposed semantic phrase identification method is promising with its accuracy being 77% in R-precision. We also show that the unsupervised method for discovering technological trends is meaningful.

• The Korean Journal of Emergency Medical Services
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• v.24 no.1
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• pp.85-92
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• 2020

Purpose: The purpose of this study was to analyze the English abstract key words found within the Korea Society of Emergency Medical Services journal using text mining techniques to determine the adherence of these terms with Medical Subject Headings (MeSH) and identify key word trends. Methods: We analyzed 212 papers that were published from 2012 to 2019. R software, web scraping, and frequency analysis of key words were conducted using R's basic and text mining packages. Additionally, the Word Clouds package was used for visualization. Results: The average number of key words used per study was 3.9. Word cloud visualization revealed that CPR was most prominent in the first half and emergency medical technician was most frequently used during the second half. There were a total of 542 (64.9%) words that exactly matched the MeSH listed words. A total of 293 (35%) key words did not match MeSH listed words. Conclusion: Researchers should obey submission rules. Further, journals should update their respective submission rules. MeSH key words that are frequently cited should be suggested for use.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.17-22
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• 2019

As the value of big data is recognized as important, it is possible to advance decision making by effectively introducing and improving the development and utilization of JAVA and R programs that can analyze vast amounts of existing and unstructured data to governments, public institutions and private businesses. In this study, news data was collated and analyzed through text mining techniques in order to establish marketing strategies based on consumers' airline preferences. This research is meaningful in establishing marketing strategies based on analysis results by analyzing consumers' airline preferences using high-level big data utilization program techniques for data that were difficult to obtain in the past.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.829-834
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• 2018

As the IT technology is progressing, the big data becomes more important and is exploited on the various industry. The R is the language and the environment analyzing the big data. The university which is the highest level of the academic organization keeps opening and maintaining the departments anticipating the needs of the progressing trends. As analyzing the names of the departments opened at the universities, it is possible to find out the requirements and the needs of the recent trends. In this paper, we analyze the names of the departments presented at the 4 year universities using the R. To do this, we collect the names of the departments and measure the frequency of the names in order to know the department of major frequently presented at the universities.