• 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.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.521-526
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• 2017

Data format of Big data is diverse and vast, and its generation speed is very fast, requiring new management and analysis methods, not traditional data processing methods. Textual mining techniques can be used to extract useful information from unstructured text written in human language in online documents on social networks. Identifying trends in the message of politics, economy, and culture left behind in social media is a factor in understanding what topics they are interested in. In this study, text mining was performed on online news related to a given keyword using topic - oriented analysis technique. We use Latent Dirichiet Allocation (LDA) to extract information from web documents and analyze which subjects are interested in a given keyword, and which topics are related to which core values are related.

• Tunnel and Underground Space
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• v.25 no.4
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• pp.303-319
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• 2015

Text mining is one of the branches of data mining and is used to find any meaningful information from the large amount of text. In this study, we analyzed titles and keywords of two SCI journals on rock engineering by using text mining to find major research area, trend and associations of research fields. Visualization of the results was also included for the intuitive understanding of the results. Two journals showed similar research fields but different patterns in the associations among research fields. IJRMMS showed simple network, that is one big group based on the keyword 'rock' with a few small groups. On the other hand, RMRE showed a complex network among various medium groups. Trend analysis by clustering and linear regression of keyword - year frequency matrix provided that most of the keywords increased in number as time goes by except a few descending keywords.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1259-1269
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• 2015

Recently, big data is positioning as a keyword in the academic circles. And usefulness of big data is carried into government, a local public body and enterprise as well as academic circles. Also they are endeavoring to obtain useful information in big data. This research mainly deals with analyses of box office success or failure of films using text mining. For data, it used a portal site 'D' and film review data, grade point average and the number of screens gained from the Korean Film Commission. The purpose of this paper is to propose a model to predict whether a film is success or not using these data. As a result of analysis, the correct classification rate by the prediction model method proposed in this paper is obtained 95.74%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.59-62
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• 2018

XML을 대상으로 하는 연구가 최근 5~6년 사이에 꾸준한 증가를 보이며 이루어지고 있지만 대다수의 연구들은 XML을 구성하고 있는 엘리먼트 자체에 대한 통계적인 모델을 기반으로 이루어졌다. 이는 XML의 고유 속성인 트리 구조에서의 텍스트, 문장, 문장 구성 성분이 가지고 있는 의미(semantics)가 명시적으로 분석, 표현되어 사용되기 보다는 통계적인 방법으로만 데이터의 발생을 계산하여 사용자가 요구한 질의에 대한 결과, 즉 해당하는 정보 및 지식을 제공하는 형식이다. 지능형 지식서비스 제공을 위한 환경에 부합하기 위한 정보 추출은, 텍스트 및 문장의 구성 요소를 분석하여 문서의 내용을 단순한 단어 집합보다는 풍부한 의미를 내포하는 형식으로 표현함으로써 보다 정교한 지식과 정보의 추출이 수행될 수 있도록 하여야 한다. 본 연구는 범람하는 XML 데이터로부터 사용자 요구의 의미까지 파악하여 정확하고 다양한 지식을 추출할 수 있는 방법을 연구하고자 한다. 레코드 구조가 아닌 트리 구조 데이터로부터 의미 추출이 가능한 효율적인 마이닝 기법을 진일보시킴으로써 다양한 사용자 중심의 서비스 제공을 최종 목적으로 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.624-626
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• 2021

Yumentingzheng, which records the contents of the Qing dynasty's discussions with his subjects, is an important document like the Annals of Joseon in Korea. This paper describes the method and steps for big data analysis of Yumentingzheng written in Manchu alphabet. In big data analysis of documents written in Manchu characters, there are many problems that need to be solved in advance, and research on these should be preceded. In this paper, a method of big data analysis using the R language was proposed in the stage where the text written in Manchurian characters was transliterated into Latin characters through a preliminary study to be conducted in the future. In the proposed method, Apkai method was adopted for the transliteration of Wumentingzheng, and the results of big data analysis were presented using the text of Weiwenqiju.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.292-297
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• 2020

자연어 추론 모델은 전제와 가설 사이의 의미 관계를 함의와 모순, 중립 세 가지로 판별한다. 영어에서는 RTE(recognizing textual entailment) 데이터셋과 다양한 NLI(Natural Language Inference) 데이터셋이 이러한 모델을 개발하고 평가하기 위한 벤치마크로 공개되어 있다. 본 연구는 국외의 텍스트 추론 데이터 주석 가이드라인 및 함의 데이터를 언어학적으로 분석한 결과와 함의 및 모순 관계에 대한 의미론적 연구의 토대 위에서 한국어 자연어 추론 벤치마크 데이터 구축 방법론을 탐구한다. 함의 및 모순 관계를 주석하기 위하여 각각의 의미 관계와 관련된 언어 현상을 정의하고 가설을 생성하는 방안에 대하여 제시하며 이를 바탕으로 실제 구축될 데이터의 형식과 주석 프로세스에 대해서도 논의한다.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.457-465
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• 2010

Customized information analysis system is a software system that can help to extract useful information from non-structured natural language data, process the information to customized form, and provide future forecast and reasoning information. To implement the information analysis system, we need natural language processing technology to analyze natural language, information extraction technology to detect necessary entity and its relationship from text, and data mining technology to discover new and unknown information from extracting data. This paper suggest virtual customized information analysis system processing national defense news data and introduce base technologies for information analysis.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.75-86
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• 2016

This study examines the social issues between the central and local governments related with the underground space after happenings of sinkholes in Jamsil area in July, 2014. In this study, we consider the keyword network of the social network analysis as a research methodology. The social issues regarding the underground space have been dealt with through the analysis of the centrality and group density to know the attributes of the network. The results show that the government has been steadily helpful to the local governments for establishing the socialized law for the underground space. This research suggests that the laws and technologies as to the underground space issues cooperate each other in the future. It also shows that the government should enact the policies and the national plans for the development of the underground.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.155-156
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• 2016

대학 교육현장에서 수많은 비정형화된 데이터가 생산되고 있는데 그중 관심 있게 볼 부분은 학생들의 서술적 강의평가이며, 본 논문에서는 대학에서 시행하는 서술적 강의평가를 활용하여 컴퓨터를 활용하는 수업에서 학생들이 원하는 상호작용을 분석하였다. 분석을 위해 빅데이터에서 활용하는 텍스트 마이닝 기법을 활용하였으며 분석결과 컴퓨터관련 관련 수업에서 필요한 학습자 상호작용은 주로 흥미, 기회, 열정, 재미, 참여, 유익, 친절 등으로 나타났다. 현재 5점 척도로 보여 지는 강의평가 점수는 진정 학습자가 원하는 것이 무엇인지 파악이 어렵기 때문에 관련 연구가 지속적으로 필요하다. 또한 향후 컴퓨터를 활용하지 않은 수업과 비교함으로써 대학 컴퓨터 관련 수업의 특징을 구분할 필요가 있을 것으로 여겨진다.