• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.2
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• pp.53-60
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• 2012

In this paper, we present new methods for identifying keywords for foresight topics that utilize the internet and textmining techniques to draw objective and quantified information that support experts' qualitative opinions and evaluations in foresight. Furthermore, by applying this fabricated procedure, we have derived keywords to analyze priorities in architectural engineering. Not much difference between qualitative methods of experts and quantitative methods such as text mining has been observed from comparison between technologies derived via qualitative method from "The Science Technology Vision" (control group). Therefore, as a quantitative tool useful for drawing keywords for foresight, textmining can supplement quantitative analysis by experts. In addition, depending on the level and type of raw data, text mining can bring better results in deriving foresight keywords. For this reason, research activities accommodating Internet search results and the development of textmining methods for analyzing current trends are in demand.

• Journal of the Korean Society of Safety
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• v.34 no.2
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• pp.48-55
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• 2019

For preventing the accidents generated from the chemical materials, thus far, MSDS (Material Safety Data Sheet) data have been made to notify how to use and manage the hazardous and chemical materials in safety. However, it is difficult for users who handle these materials to understand the MSDS data because they are only listed based on the alphabetical order, not based on the specific factors such as similarity of characteristics. It is limited in representing the types of chemical materials with respect to their characteristics. Thus, in this study, a lots of MSDS data are visualized based on relationships of the characteristics among the chemical materials for supporting safety managers. For this, we used the textmining algorithm which extracts text keywords contained in documents and the Self-Organizing Map (SOM) algorithm which visually addresses textual data information. In the case of Occupational Safety and Health Administration (OSHA) in the United States, the guide texts contained in MSDS documents, which include use information such as reactivity and potential risks of materials, are gathered as the target data. First, using the textmining algorithm, the information of chemicals is extracted from these guide texts. Next, the MSDS map is developed using SOM in terms of similarity of text information of chemical materials. The MSDS map is helpful for effectively classifying chemical materials by mapping prohibited and hazardous substances on the developed the SOM map. As a result, using the MSDS map, it is easy for safety managers to detect prohibited and hazardous substances with respect to the Industrial Safety and Health Act standards.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.721-732
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• 2021

This study analyzed keywords for agricultural R&D using the textmining method to examine the trend of agricultural R&D. Data used for the analysis included R&D project information provided by NTIS, and the research and development step by year from 2003 to 2018 were classified and applied. The TF-IDF approach was used as the analysis method, and ranking was derived based on score. Furthermore, we analyzed by grouping for similar keywords. The main analysis results are as follows. First, agricultural R&D trends are changing according to the introduction of new technologies and changes in the external environment. Second, keyword changes appeared with a time lag in the R&D step. The main keywords are changing in the order of basic research - applied research - development research. Third, the main keyword of agricultural R&D was 'rice.' However, the direction and purpose of the research were changing according to changes in the domestic and foreign agricultural environments.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.219-224
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• 2022

The Korea Meteorological Administration(KMA) has been released life-related indexes such as 'Life industrial weather information' and 'Safety weather information' while other countries' meteorological administrations have been made 'Human-biometeorology' and 'Health meteorology' indexes that concern human sensibility effections to diverse criteria. Although human sensibility changes have been studied in psychological research criteria with diverse and innumerous application areas, there are not enough studies that make data mining based validation of sensibility change factors. In this research I made models to estimate sensibility change caused by weather factors such as temperature and humidity, and validated by collecting sensibility data from SNS text crawling and weather data from KMA public dataset. By Logistic Regression, I clarify factors affecting sensibility changes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.253-255
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• 2020

기업 및 기관 데이터는 워드프로세서, 프레젠테이션, 이메일, open api, 엑셀, XML, JSON 등과 같은 텍스트 기반의 비정형 데이터로 구성되어 있습니다. 텍스트 마이닝(Textmining)을 통해서 자연어 처리 및 기계학습 등의 기술을 이용하여 정보의 추출부터 요약·분류·군집·연관도 분석 등의 과정을 수행울 진행한다. 다양한 시각화 데이터를 보여줄 수 있는 다양한 모델 구축을 진행한 후 민원 신청 내용을 분석 및 변환 작업을 진행한다. 본 논문은 AI 기술과 빅데이터를 활용하여 민원을 분석을 하여 알맞은 부서에 민원을 자동으로 할당해 주는 기술을 다룬다.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.678-679
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• 2015

Recently, safety accidents in construction sites are increasing. Accordingly, in this study, development of 'Big-Data Analysis Modeling' can collect articles from last 10 years which came from the Internet News and draw the cause of accidents that happening per season. In order to apply this study, Web Crawling Modeling that can collect 98% of desired information from the internet by using 'Xml', 'tm', "Rcurl' from the library of R, a statistical analysis program has been developed, and Datamining Model, which can draw useful information by using 'Principal Component Analysis' on the result of Work Frequency of 'Textmining.' Through Web Crawling Modeling, 7,384 out of 7,534 Internet News articles that have been posted from the past 10 years regarding "safety Accidents in construction sites", and recognized the characteristics of safety accidents that happening per season. The result showed that accidents caused by abnormal temperature and localized heavy rain, occurred frequently in spring and winter, and accidents caused by violation of safety regulations and breakdown of structures occurred frequently in spring and fall. Plus, the fact that accidents happening from collision of heavy equipment happens constantly every season was acknowledgeable. The result, which has been obtained from "Big-Data Analysis Modeling" corresponds with prior studies. Thus, the study is reliable and able to be applied to not only construction sites but also in the overall industry.

• Bioinformatics and Biosystems
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• v.2 no.1
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• pp.33-36
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• 2007

Scientific literature is the most reliable and comprehensive source of knowledge for scientific and biomedical information. Citation information in the literature is also reliable source for linking between literatures. We proposed CiNet, a graphic user interface based tool that extracts the trend of the research using citation information. We can navigate related literatures and extract keywords from the linked literature using this tool. These extracted keywords will be helpful to researchers who want to survey the information.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.1
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• pp.191-207
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• 2017

This paper employs entitymetrics analysis on the research works of Dong-ju Yun. He was a Korean poet who was studied by many researchers on his works, religion and life. We collected 1,076 papers about Dong-ju Yun and conducted various approaches including co-author citation analysis, topic modeling analysis to identify the topic trend in the study of Dong-ju Yun. Also we extracted entities like person's name and literature's title from abstract to examine the relationship among them. The result of this paper enables us to objectively identify the topic trend and infer implicit relationships between key concept associated with Dong-ju Yun based on text data. Moreover, we observed sub-research topics such as life, poem, aesthetic existence, comparative literature, literary translation, and religious beliefs. This paper shows how entitymetrics can be utilized to study intellectual structures in the humanities.

• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.58-64
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• 2018

Recently, a lot of accident report documents have accumulated in almost all of industries, including critical information of accidents. Accordingly, text data contained in accident report documents are considered useful information for understanding accident processes. However, there has been a lack of systematic approaches to analyzing accident report documents. In this respect, this paper aims at proposing text analytics approach to extracting critical information on accident processes. To be specific, major causes of the accident occurrence are classified based on text information contained in accident report documents by using both textmining and latent Dirichlet allocation (LDA) algorithms. The textmining algorithm is used to structure the document-term matrix and the LDA algorithm is applied to extract latent topics included in a lot of accident report documents. We extract ten topics of accidents as accident types and related keywords of accidents with respect to each accident type. The cause-and-effect diagram is then depicted as a tool for navigating processes of the accident occurrence by structuring causes extracted from LDA. Further, the trends of accidents are identified to explore patterns of accident occurrence in each of types. Three patterns of increasing to decreasing, decreasing to increasing, or only increasing are presented in the case of a chemical plant. The proposed approach helps safety managers systematically supervise the causes and processes of accidents through analysis of text information contained in accident report documents.