• Journal of Information Management
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• v.31 no.3
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• pp.1-20
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• 2000

A variety of trials selecting necessary and valuable information from rapidly increasing volume of data are made, and as one of them, data mining methods is an interest. This methodology is increasingly appzied to information management field which consists of efficient processing and systemizing increasing digital documents for user service. This article analyzes theoletical background and empirical case studies of data mining, and predicts the possibility of its application to information management area.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.147-165
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• 2006

In this study, experiments for selection of association terms were conducted in order to discover the optimum method in selecting additional terms that are related to an initial query term. Association term sets were generated by using support, confidence, and lift measures of the Apriori algorithm, and also by using the similarity measures such as GSS, Jaccard coefficient, cosine coefficient, and Sokal & Sneath 5, and mutual information. In performance evaluation of term selection methods, precision of association terms as well as the overlap ratio of association terms and relevant documents' indexing terms were used. It was found that Apriori algorithm and GSS achieved the highest level of performances.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.103-104
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• 2021

본 논문에서는 텍스트 마이닝 기반의 토픽 모델링 웹 애플리케이션 모델을 제안한다. 웹크롤링 기법을 활용하여 키워드를 입력하면 요약된 논문 정보를 파일로 저장할 수 있고 또한 키워드 빈도 분석과 토픽 모델링 등을 통해 연구 동향을 손쉽게 확인해볼 수 있는 웹 애플리케이션을 설계하고 구현하는 것을 목표로 한다. 제안 모델인 웹 애플리케이션을 통해 프로그래밍 언어와 데이터 분석 기법에 대한 지식이 부족하더라도 논문 수집과 저장, 텍스트 분석을 경험해볼 수 있다. 또한, 이러한 웹 시스템 개발은 기존의 html, css, java script와 같은 언어에 의존하지 않고 파이썬 라이브러리를 활용하였기 때문에 파이썬을 기반으로 데이터 분석과 머신러닝 교육을 수행할 경우 프로젝트 기반 수업 교육 과정으로 채택이 가능할 것으로 기대된다.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.543-550
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• 2019

In 2006, Janet Wing defined computational thinking and operated SW education as a formal curriculum in the UK in 2013. This study collected related research papers by using computational thinking, which has recently increased in importance, and analyzed it using text mining. In the first, CONCOR analysis was conducted with the keyword of computational thinking. In the second, text mining of the components of computational thinking was selected by the repr23esentative academic journals at domestic and foreign. As a result of the two-time analysis, first, abstraction, algorithm, data processing, problem decomposition, and pattern recognition were the core of the study of computational thinking component. Second, research on convergence education centered on computational thinking and science and mathematics subjects was actively conducted. Third, research on computational thinking has been expanding since 2010. Research and development of the classification and definition of computational thinking and components and applying them to education sites should be conducted steadily.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.13-24
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• 2018

For government and public institutions, civil complaints containing direct requirements of citizens can be utilized as important data in developing policies. However, it is difficult to draw accurate requirements using text mining methods since the nature of the complaint text is unstructured. In this study, a new method is proposed that draws the exact requirements of citizens, improving the previous text mining in analyzing the data of civil complaints. The new text-mining method is based on the principle of Co-Occurrences Structure Map, and it is structured by two-step association analysis, so that it consists of the first-order related word and a second-order related word based on the core subject word. For the analysis, 3,004 cases posted on the electronic bulletin board of Busan City for the year 2016 are used. This study's academic contribution suggests a method deriving the requirements of citizens from the civil affairs data. As a practical contribution, it also enables policy development using civil service data.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.619-630
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• 2015

The purpose of this study is to provide basic information to develop a game strategy plan of a team in a future by identifying the patterns of attack and pass of national men's professional volleyball teams and extracting core key words related with volleyball game performance to evaluate game performance using 'social network analysis' and 'text mining'. As for the analysis result of 'social network analysis' with the whole data, group '0' (6 players) and group '1' (11 players) were partitioned. A point of view the degree centrality and betweenness centrality in 'social network analysis' results, we can know that the group '1' more active game performance than the group '0'. The significant result for two group (win and loss) obtained by 'text mining' according to two groups ('0' and '1') obtained by 'social network analysis' showed significant difference (p-value: 0.001). As for clustering of each network, group '0' had the tendency to score points through set player D and E. In group '1', the player K had the tendency to fail if he attack through 'dig'; players C and D have a good performance through 'set' play.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.88-106
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• 2022

This study examined the social discourse on consumer boycott and explored consumer experience using text mining of mass media and social media data and the in-depth interview. The result showed that the topics of online news related to the boycott included the causes of the boycott, the responses of each actor in the process of the boycott, and the effects of the boycott. In the result of the in-depth interviews, it was found that the boycott has been decentralized and the participants had the experience of exploring and verifying information on their own. In the boycott process, there were mixed experiences due to the absence of substitutes and the marketing influence, and positive experiences of expressing one's thoughts and strengthening beliefs through the boycott.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.151-161
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• 2016

In the era of the Web 2.0, characterized by the openness, sharing and participation, it is easy for internet users to produce and share the data. The amount of the unstructured data which occupies most of the digital world's data has increased exponentially. One of the kinds of the unstructured data called personal online product reviews is necessary for both the company that produces those products and the potential customers who are interested in those products. In order to extract useful information from lots of scattered review data, the process of collecting data, storing, preprocessing, analyzing, and drawing a conclusion is needed. Therefore we introduce the text-mining methodology for applying the natural language process technology to the text format data like product review in order to carry out extracting structured data by using R programming. Also, we introduce the data-mining to derive the purpose-specific customized information from the structured review information drawn by the text-mining.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.355-360
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• 2014

Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts' evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.17-20
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• 2011

축구 비디오를 분석하고 이를 팀 전략 수립에 활용하는 축구 비디오 분석관의 역할이 강조됨에 따라, 축구 비디오에서 주요 이벤트의 탐지와 같은 절차적 기능에서 부터 고수준의 해석 방법에 이르는 다양한 기능들이 요구된다. 본 논문에서는 축구 웹 캐스트에서 실시간으로 제공하는 텍스트 정보를 기반으로 메타데이터 키워드 매칭을 통하여 축구 경기의 다양한 속성들을 추출하고 텍스트 마이닝의 대표적 해석 기법인 연관규칙 마이닝을 사용함으로써 축구 경기의 전략 수립이 가능한 고수준의 해석 방법을 소개한다. 실제 2010년 월드컵의 스페인 경기를 중계한 웹 캐스트의 텍스트 정보를 대상으로 제안된 방법론의 타당성을 검증한다.