• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.173-180
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• 2018

In this paper, we analyzed key keywords and research themes in the field of defense research using keyword network analysis and tried to grasp the whole knowledge structure. To do this, we extracted data from 2,165 research data from defense related research institutes from 2010 to 2017 and applied the Pareto rule to the number of abstracts of words and the number of links between words, We extracted a total of 2,303 words based on the criterion and extracted 204 final key words through component analysis. By analyzing the centrality and cohesiveness through these key words, we confirmed the concept of core research in the defense field and derived a total of 7 large groups and 16 small groups of each group in the knowledge structure of the defense area.

• Journal of information and communication convergence engineering
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• 제19권1호
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• 한국정보통신학회논문지
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• 제24권5호
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• pp.670-673
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• 2020

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

• 한국콘텐츠학회논문지
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• 제13권9호
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• pp.440-447
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• 2013

코퍼스 언어학이 연구방법의 한 분야로서 최근 그 입지를 급격하게 넓혀온 가운데, 언어학적 현상과 함께 문학텍스트의 이해를 깊게 하는데 기여를 해 왔다. 최근 코퍼스 언어학의 급속한 저변확대에도 불구하고 문학텍스트 코퍼스를 기반으로 한 고전 및 문학작품의 재해석에 대한 시도는 국내언어학계에서 매우 미미한 실정에 머물러 있다. 이에 본 연구는 코퍼스 언어학의 분석도구인 컴퓨터 콘코던스 프로그램인 워드스미스를 이용하여 방대한 전자텍스트로 이루어져 있는 문학작픔의 문체적 특성과 주요테마를 조사하고자 하였다. 특히 본 연구는 텍스트의 주요한 특성을 나타내는 키워드(keyword)에 초점을 두고 세익스피어의 비극작품인 로미오와 줄리엣을 코퍼스 언어학적 분석기법으로 접근하여 작품세계를 재조명하여 학문적 의의가 크다고 생각되며 앞으로 관련된 후속연구가 이어질 것으로 기대된다.

• 한국산학기술학회논문지
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• 제5권1호
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• pp.44-48
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• 2004

본 논문에서는 자연언어 처리 기술인 구문 분석 모듈을 도입해 단어 이상의 단위인 구 단위를 색인과 검색의 단위로 삼는 구 단위 색인 및 검색 기법의 사용을 제안한다. 초기의 정보검색의 방법으로 단일 주제어를 키워드로 색인하여 검색하는 방식이 널리 사용되어 왔으나 문서의 내용을 정확히 표현하기 어렵고 검색 결과의 문서 집합 또한 너무 커서 사용자의 만족도가 낮다 고도의 문서 처리 측면에서는 웹 문서들 자체가 갖는 다양한 오류들로 인해 현실적으로 충분히 만족할 만할 우수한 성능의 구문 분석 모듈이 구현되기는 어려우므로 상향식 구문 분석 모듈을 구현하여 완전한 구문 분석 결과를 얻지 못하는 많은 문장에 대해서도 가능한 구 단위 색인을 이용하여 검색 정확률과 재현률이 향상되고 검색 과정의 처리 부하도 줄이는 장점을 얻는다.

• 한국IT서비스학회지
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• 제21권3호
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• 한국의상디자인학회지
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• 제24권4호
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• pp.73-85
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• 2022

The purpose of this study is to conduct more practical subsequent research by identifying research areas through a systematic analysis of sustainable fashion design research trends. For this study, 117 journals domestic journals published between 2010 and 2020 were selected using the keyword, 'sustainable fashion'. With the research materials, six top keywords, 'zero waste', 'sustainability', 'eco-friendly', 'upcycling', 'recycling', and 'ethical', were derived. The research status was examined by year, keyword, keyword and year, and research topic. The analysis results are as follows. First, looking into the studies by year, it was found that research on sustainable fashion increased in general. Compared to 2010, the research tripled in 2020, and it was found to have increased steadily from 2018. Second, regarding the research by keyword, eco-friendly was the most common. It can be seen that research tended to focus on recycling or eco-friendliness before, but in later material design development was heading towards upcycling. Third, concerning the research by topic, case studies were found the most before, but research on design development tended to increase recently. Based on that, it is expected that the areas of sustainable fashion design that need more research will be investigated further.

• 산업경영시스템학회지
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• 제41권1호
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• 정보처리학회논문지D
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• 제19D권4호
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• pp.263-270
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• 2012

최근 초고속 인터넷과 대용량 데이터베이스 기술의 발전으로 웹 문서의 양이 크게 증가하였으며, 이를 효과적으로 관리하기 위하여 문서의 주제별 자동 분류가 중요한 문제로 대두되고 있다. 본 연구에서는 한글 형태소 및 키워드 분석에 기초한 문서 특성 추출 방법을 제안하고, 이를 이용하여 웹 문서와 같은 비구조적 문서의 주제를 예측하여 문서를 자동으로 분류하는 방법을 제시한다. 먼저, 문서 특성 추출을 위하여 한글 형태소 분석기를 사용하여 용어를 선별하고, 각 용어의 빈도와 주제 분별력을 기초로 주제 분별 용어인 키워드 집합을 생성한 후, 각 키워드에 대하여 주제 분별력에 따라 점수화한다. 다음으로, 추출된 문서 특성을 기초로 상용 소프트웨어를 사용하여 의사 결정 트리, 신경망 및 SVM의 세 가지 분류 모델을 생성하였다. 실험 결과, 제안한 특성 추출 방법을 이용한 문서 분류는 의사 결정 트리 모델의 경우 평균 Precision 0.90 및 Recall 0.84 로 상당한 정도의 분류 성능을 보여 주었다.

• 한국IT서비스학회지
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• 제17권2호
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• pp.143-163
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• 2018

The business models has a great impact on the successful management of enterprises. Business environment has been shifting from industrial economy to knowledge-based economy. Enterprises go through numerous trials for successful management in the changing environment. Along with trial tests, research areas have been growing simultaneously. Although many researches have been conducted with regard to business models, it is very insufficient to systematically analyze the knowledge flow of research. Accordingly, successive researchers who want to study the business model may find it difficult to establish the orientation of future application research based on understanding the process of changing the knowledge structure that have accumulated so far. This study is intended to determine the current state of the business model research and to understand the process of knowledge structure changes in keywords that appear in 2,667 business model articles in the SCOPUS database. Identifying the knowledge structure has been completed through social network analysis, a methodology based on the 'relationship', and the changes in the knowledge structure were identified by classifying them into four different periods. The analysis showed that, first, the number of business model co-author increases over time with the need for academic diversity. Second, the 'innovation' keyword has the biggest center in the network, and over time, the lower-rank keyword which was in the former period has emerged as the top-rank keyword. Third, the cohesiveness group decreased from 12 before 2000 to 5 in 2015 and also the modularity decreased as well. Finally, examining characteristics of study area through a cognitive map showed that the relationships between domains increased gradually over time. The study has provided a systematic basis for understanding the current state of the business model research and the process of changing knowledge structure. In addition, considering that no research has ever systematically analyzed the knowledge structure accumulated by individual researches, it is considered as a significant study.