• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• Aerospace Engineering and Technology
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• v.7 no.2
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• pp.11-20
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• 2008

The current air-to-ground communication between aircraft pilots and ground controllers is done by voice communication and text-based data communication. International Civil Aviation Organization (ICAO) suggested the digital data communication techniques to improve accuracy and effectiveness of the current air-to-ground communication. As one of them, VDL M2, a VHF band digital data communication link, is expected to substitute the voice communication and text-based ACARS data communication. In this paper, the software design and implementation of the VDL M2 system developed by Korea Aerospace Research Institute.

• Digital Contents
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• no.1 s.56
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• pp.84-91
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• 1998

데이터베이스에 저장되는 데이터의 양은 급격히 증가되어 왔고, 사용자들은 필요한 데이터를 찾기 위해 서말이 넘는 땀방울을 흘려야 했다. 그러나 새로운 탐색 기술들이 이러한 문제에 대한 해답을 제시하고 있다. 자연언어 질의, 역-인덱싱, 인터넷 탐색 엔진, 데이터베이스 텍스트 탐색 같은 기술들은 사용자의 오랜 숙원을 해결해줄 기대주로 관심을 모으고 있다. 자유 형식 정보 소스들로부터 정보의 특별한 탐색과 추출을 수행하기 위해 사용 가능한 보다 새로운 탐색 기술들과 기법들을 소개한다.

• Digital Contents
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• no.7 s.74
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• pp.33-35
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• 1999

데이터 관리 및 교환 분야의 국제 표준화를 추진하고 있는 JTC1/SC32의 총회 및 작업반 회의가 5월24일부터 28일까지 일본 마츠에에서 개최되었다. 이번 총회는 9개국에서 52명의 전문가가 참가하였으며, 텍스트 및 수치 데이터를 포함하여 그래픽 데이터를 처리할 수 있는 새로운 데이터베이스 언어인 SQL3의 국제 표준이 채택되었다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.232-234
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• 2006

인터넷 기술의 발달과 디지털 카메라와 같은 디지털 미디어 생산 장비의 발달로 WWW에 이미지 데이터의 양이 급격하게 늘어나면서 웹 이미지에 대한 효율적인 검색에 대한 요구가 증가하고 있다. 본 논문에서는 사용자의 다양한 검색 요구를 만족시킬 수 있도록 기존의 텍스트 기반의 검색과 시각 정보 기반의 검색을 병합하여 수행할 수 있는 웹 이미지 검색 시스템을 설계하고 구현한다. 제안한 웹 이미지 검색 시스템은 웹 이미지 수집 및 검색정보 추출 도구. 검색 서버. 그리고 검색 클라이언트로 구성된다. 웹 이미지 수집 및 검색 정보 추출 도구는 웹에서 이미지를 수집하여 이미지가 속해있는 웹 문서 구조를 이용하여 적절한 키워드를 선택하며 시각 정보 기반의 검색을 지원하기 위해 MPEG-7 시각 정보 기술자(1)를 추출한다. 빠른 검색을 위해 추출된 텍스트 정보는 상용 데이터베이스에 저장되며 MPEG-7 시각 정보 기술자는 고차원 데이터 색인 방법인 HBI (Hierarchical Bitmap Index)(2)를 사용하여 색인 정보를 만들어 사용한다. 검색 클라이언트는 사용자가 각 검색 요소에 가중치를 부여하여 검색 할 수 있도록 하며 원하는 검색 결과를 얻을 때까지 반복하여 검색할 수 있는 연관 피드백 과정도 포함한다.

• 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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• 2016.10a
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• pp.448-450
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• 2016

정보기술과 디지털 경제의 확산으로 대규모의 데이터가 생산되는 정보화시대에서 빅 데이터의 중요성이 강조되고 있으며 다양한 분야에서 이를 응용하고 있다. 빅 데이터 분석도구인 R은 통계 기반의 정보 분석을 가능하게 하는 언어와 환경이다. 본 논문에서는 R을 이용하여 기상뉴스에 나타난 기상관련 빅 데이터를 분석한다. 다양한 뉴스에서 기상 관련 데이터를 수집하고 어떠한 텍스트가 분포되어 있는지 빈도 조사를 수행한다.

• 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 Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.167-176
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• 2021

With development of big data analysis technology, many studies to find social issues using texts mining techniques have been conducted. In order to derive social issues, previous studies performed in a way that collects a large amount of text data from news or SNS, and then analyzes issues based on text mining techniques such as topic modeling and terms network analysis. Social issues are the results of various social phenomena and factors. However, since previous studies focused on deriving social issues that are results of various causes, there are limitations to revealing the cause of the issues. In order to effectively respond to social issues, it is necessary not only to derive social issues, but also to be able to identify the causes of social issues. In this study, in order to overcome these limitations, we proposed a method of deriving the factors that cause social issues from texts related to social issues based on the theory of part of Korean linguistics. To do this, we collected news data related to social issues for three years from 2017 to 2019 and proposed a methodology to find causes based causal sentences based on text mining techniques.