• 한국의류학회지
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• 제41권6호
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• pp.1060-1070
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• 2017

This study seeks methods to analyze users' perception in fashion designs shown in social media using textmining analysis methods. The research methods selected 'men's stripe shirts' as subjects and collected texts related to the subject mainly from blogs. Texts from 13,648 posts from November 1st, 2015 to October 31st, 2016 were analyzed by applying the LDA algorithm and content analysis. As a result, the wearing status per season and subjects of men's stripe shirts were derived. Across the entire period, the main topics discussed by users to be pattern, customized suits, brands, coordination and purchase information. In terms of seasons, spring time showed the sharing of information on coordinating daily looks or boyfriend looks, and during the winter season the information shared were about shirts suitable for special occasions such as job interviews and stripe shirts that match suits. The study results showed that text-mining analysis is capable of analyzing the context and provide a user-centered index responding to demands newly mentioned by users along with the rapid changes in fashion design trends.

• Human Ecology Research
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• 제59권2호
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• pp.261-273
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• 2021

This study is not just a meaning of costume but a function of culture and includes addresses the associated emotions. As the interest of youths has increased recently, the importance of traditional costume education has been growing. Therefore, this study aims to analyze the contents of Hanbok in the 2015 revised high school technology and home textbooks using content analysis techniques and text mining techniques. As a result of the study, first, the symbolic meaning and characteristics of Hanbok and the beauty of Hanbok were practiced in daily life, and the value was found through the excellence of Hanbok and the modernization of Hanbok was dealt with Second, most of the illustrations related to traditional costumes were presented in various ways, but there were some regrets due to lack of quantity and quality. Third, the words used to explain traditional costumes were used in the form of culture, excellence, tradition, modernity, harmony, succession, etc. except for the types of clothing. Therefore, the results and discussions derived from this study are expected to help the textbooks to be efficiently selected and used in the field of the front line school along with the correct understanding of traditional culture in the process of selecting traditional culture contents and illustrations.

• 한국산업정보학회논문지
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• 제23권3호
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• pp.13-24
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• 2018

정부 및 공공기관에 있어 시민의 직접적인 요구사항이 담겨 있는 민원은 정책 개발을 위한 중요한 데이터로 활용이 가능하다. 그러나 민원 데이터는 비정형 텍스트로 작성되어 있는 특성으로 인해 일반적인 텍스트 마이닝 기법으로는 시민의 요구사항을 정확히 도출하기 어려웠다. 이에 본 연구에서는 민원 데이터 분석을 위한 텍스트 마이닝 기법을 개선하여, 시민의 요구사항을 도출할 수 있는 방법을 제시하고자 하였다. 새로운 텍스트 마이닝 기법은 공기어구조맵의 원리에 착안하여 연관성 분석을 2단계로 실시하여 핵심주제어를 기반으로 1차 연관 단어 와 2차 연관 단어로 구조화하였다. 분석을 위해 2016년 1년간 부산시 민원게시판에 올라온 3004건을 활용하였다. 분석 결과는 빈도수와 핵심주제어를 가지고 연관성 분석만으로는 찾을 수 없었던 민원 상의 문제를 본연구에서 제시한 계층적 연관성 분석을 이용하여 시민의 요구사항을 더욱 정확하게 파악할 수 있었다. 본 연구는 민원 데이터에서 시민의 요구사항을 도출하기 용이한 방법을 제안하였다는 학문적 기여점이 있으며, 행정기관에서 민원 데이터를 통해 정책 개발에 활용할 수 있다는 실무적 기여점이 있다.

• 대한산업공학회지
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• 제40권1호
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• pp.52-59
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• 2014

We survey 3,857 journal articles published on the four domestic academic journals in the industrial engineering field during 1975~2012. Titles, abstracts, and keywords of the papers are searched by means of text mining technique to draw the information on the methodologies and techniques adopted in the papers, and then we aggregate and merge similar ones to obtain final 38 representative methodologies and techniques. Trends of these methodologies and techniques are studied by analyzing frequencies, clustering, and finding association rules among them. Results of the paper can shed a light to choose tools in the future education and research in the industrial engineering related area.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.183-190
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation, which means changes in all industrial structures, politics, economics and society as well as IT technology, is an important issue. It is difficult to know which research topic is being studied because digital transformation is being studied in various fields. Convergence research is possible because a research topic is studied in various fields such as computer science area and Decision science area. However, it is difficult to know the specific research status of the research topic. In this study, eight research topics were derived using the topic modeling technique of text mining for abstract of academic literature and the trend of each topic was analyzed. We also proposed to create a Topic-Word Proportions Table in the LDA based Topic modeling process to predict the topic of new literature. The results of this study are expected to contribute to advanced convergence research on topic of digital transformation. It is expected that the literature related to each research topic will be grasped and contribute to the design of a new convergence research.

• Educational Technology International
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• 제23권1호
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• pp.97-127
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• 2022

With the recent advancement of technology, there has been an increase in professional development activities, including teachers using blogs to share practical knowledge and reflect on teaching and learning. This study was conducted to identify the contents of practical knowledge shared through the K-12 teachers' blogs. To achieve the research objective, 70,571 blog posts were collected from 329 blogs of K-12 teachers in Korean and analyzed using text mining techniques. The results of the study are as follows. First, practical knowledge sharing activities using teacher blogs have increased. Teachers posted a lot of blogs during the semester. Second, primary school teachers share various curriculum activities, reflections on project classes, class management, opinions related to education, and personal. Third, secondary school teachers share summaries and reviews of curriculum, materials related to college entrance exams, various instructional materials, opinions related to education, and personal experiences on their blogs. This study suggested that blogs are widely used as a venue for sharing practical knowledge of teachers, and that blogs can be a useful way to develop professionalism.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.39-40
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• 2022

The use of asbestos has been completely prohibited in Korea since 2015. Therefore, nationally, the asbestos demolitions in the building are actively underway. In the process of demolishing asbestos, scattering dust occurs, which poses a risk to human body. These dusts causes fatal disease, and especially there is an increasing concern of safety about construction workers and building users. Until this day, however, only few researches have been conducted on asbestos demolishing process. Accordingly, it is necessary to analyze key factors and to develop a safety prediction model for workers. This study is an early stage of building quantified DB, and aims to actualize the safety problems of asbestos demolishing process using text mining method.

• Journal of Information Technology Applications and Management
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• 제25권1호
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• 디지털융복합연구
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• 제18권3호
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• pp.1-12
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• 2020

본 연구의 목적은 텍스트 마이닝을 활용하여 특허와 뉴스 기사 분석을 통해 블록체인 기술 동향을 탐색하고 사회적 관심을 파악하여 블록체인 정책의제를 제안하는 것이다. 이를 위해 국내 블록체인 특허 요약문 327건과 온라인 뉴스기사 전문 5,941건을 수집하고 전처리 과정을 거쳐 LDA 토픽모델링 방법을 사용하여 특허 토픽 12개와 뉴스 토픽 19개를 추출하였다. 특허 분석을 통해 인증과 거래 관련 토픽이 높은 비중을 차지하였다. 뉴스 기사 분석 결과, 사회적 관심은 암호화폐에 치중되어 있는 것으로 나타났다. 이러한 분석 결과와 의제설정이론에 근거하여 블록체인 관련 정책의제를 도출하였다. 본 연구는 대용량 텍스트 문서 분석의 자동화된 기법을 활용하여 분석을 효율적·객관적으로 수행하였으며, 블록체인 기술 동향과 사회적 관심도를 파악한 실증된 기초 분석 자료를 기반으로 정책의제를 제안하였다. 본 연구에서 제시된 정책의제는 향후 정책 결정과정에의 기초자료로 활용될 수 있을 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제26권4호
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.