• Journal of Digital Convergence
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• v.16 no.11
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• pp.1-15
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• 2018

The purpose of this study is to analyze bibliographic information of Busan and other cities' reports for urban development initiative and identify the strategic fields for future smart city plan. Text mining method is used in this study to extract keywords and identify the characteristics and patterns of information in urban development reports. As a result, in earlier stage, Busan city focused on service creation for industrial development but there are lack of discussions on the linkage of information systems with ICT technology. However, recent urban planning in Busan contained various contents related to integrated connections of infrastructure, ICT system, and operation management of city in the specific fields of traffic, tourism, welfare, port/logistics, culture/MICE. This results of study is expected to provide policy implications for planning the future urban initiatives of smart city development.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.33-43
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• 2012

Semantic web is utilized to construct advanced information service by using semantic relationships between entities. Text mining can be applied to generate semantic relationships from unstructured data resources. In this study, ontology schema guideline, ontology instance generation, disambiguation of same name by text mining and advanced historical people finding service by reasoning have been proposed. Various relationships between historical event, organization, people, which are created by domain experts, are linked to literatures of National Institute of Korean History (NIKH). It improves the effectiveness of user access and proposes advanced people finding service based on relationships. In order to distinguish between people with the same name, we compares the structure and edge, nodes of personal social network. To provide additional information, external resources including thesaurus and web are linked to all of internal related resources as well.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.10-23
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• 2016

With requiring circumspections using big data, this study attempts to develop and apply the search method for audit issues relating to the ITS policy or program. For the foregoing, the auditing process of the board of audit and inspection was converged with the theoretical frame of boundary analysis proposed by William Dunn as an analysis tool for audit issues. Moreover, we apply the text mining technique in order to computerize the analysis tool, which is similar to the boundary analysis in the concept of approaching meta-problems. For the text mining analysis, specific model we applied the antisymmetry-symmetry compound lexeme-based LDA model based on the Latent Dirichlet Allocation(LDA) methodologies proposed by David Blei. The several prime issues were founded through a case analysis as follows: lack of collection of traffic information by the urban traffic information system, which is operated by the National Police Agency, the overlapping problems between the Ministry of Land, Infrastructure and Transport and the Advanced Traffic Management System and fabrication of the mileage on digital tachograph.

• 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.

• 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 Digital Convergence
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• v.18 no.8
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• pp.37-48
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• 2020

This study aimed at understanding trends of living lab studies and deriving implications for directions of the studies by utilizing text mining. The study included network analysis and topic modelling based on keywords and abstracts from total 166 thesis published between 2011 and November 2019. Centrality analysis showed that living lab studies had been conducted focusing on keywords like innovation, society, technology, development, user and so on. From the topic modelling, 5 topics such as "regional innovation and user support", "social policy program of government", "smart city platform building", "technology innovation model of company" and "participation in system transformation" were extracted. Since the foundation of KNoLL in 2017, the diversification of living lab study subjects has been made. Quantitative analysis using text mining provides useful results for development of living lab studies.

• Communications of Mathematical Education
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• v.32 no.4
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• pp.555-573
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• 2018

The purpose of this study is to identify the emotional language of math teachers in math class using text mining techniques. For this purpose, we collected the discourse data of the teachers in the class by using the excellent class video. The analysis of the extracted unstructured data proceeded to three stages: data collection, data preprocessing, and text mining analysis. According to text mining analysis, there was few emotional language in teacher's response in mathematics class. This result can infer the characteristics of mathematics class in the aspect of affective domain.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.49-55
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• 2019

This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• pp.74-75
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• 2017

In this paper, we analyzed 3,668 papers of JKIICE from 2007 to 2016 using text mining methods for understanding research fields. We used web scraping programs of Python language for data collection, and utilized topic modeling methods based on LDA algorithm implemented by R language. In the results, we verified that representative research areas of JKIICE could be downsized to 9 areas only by the analysis though the submission areas were 19 areas by 2016.

• Proceedings of the Korea Information Processing Society Conference
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• pp.189-192
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• 2010

분자 생물학을 통한 연구가 심화되면서, 생물학 정보는 기하급수적으로 늘어나고 있다. 그에 따라 바이오메디컬(생물학, 의학) 관련 논문들의 출판 및 등록 건수도 해마다 증가하고 있다. 그러나 바이오메디컬 문서들에서 유용한 정보를 추출하는 기술은 이러한 분야의 전문가 큐레이터(curator)에 의존한 경우가 많아서, 그 작업의 속도와 양적인 면에서 한계를 가지고 있다. 이러한 이유 때문에 바이오메디컬 문서를 기계학습을 통하여 분석하는 기법이 도입되기 시작하였다. 아직까지는 기계학습을 이용하여 구축된 데이터베이스가 소수에 불과하지만, 점차 증가하는 추세에 있다. 이러한 현 추이를 분석하고 향후의 추세를 예측하고자 텍스트마이닝 기술이 생물학과 의학 분야에서 어떻게 사용되며, 그 정보들이 어떻게 관리되는지 연구, 조사 하게 되었다. 현재 바이오메디컬 관련 데이터베이스들이 여러 기관 및 단체에 의해 구축 및 관리되고 있으며, 국가적인 프로젝트로서 이러한 데이터베이스들을 통합하는 과정을 진행하고 있다. 이처럼 국가기관의 주도하에 데이터베이스를 통합하여 관리하고자 하는 노력들이 계속되고 있어, 앞으로는 바이오메디컬 자료들을 검색하기가 보다 용이해질 것으로 생각된다. 텍스트마이닝을 이용하여 바이오메디컬 정보들을 추출하는 기술은 초기에는 공동 발생(co-occurence)과 같이 단순한 통계적 방법을 이용하였지만, 최근에는 다른 문서에서 추출된 정보와 기존의 정보들을 연계하여 새로운 정보를 추출해 내는 기법이 확산되고 있음을 알 수 있었다.