• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.115-116
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• 2021

5세대(5G) 이동통신망의 보급과 코로나19 여파로 비대면 시대가 열리면서 가상 증강현실(VR·AR)을 기반으로 한 '실감(XR·Extended Reality)경제가 본격화 되었다. 가상증강현실의 적용분야로는 게임·영화 등 엔터테인먼트, 제조업, 쇼핑 및 전자상거래, 병원·의료기기, 고객서비스, 지식서비스교육 분야 등이 있다. 본 논문은 VR·AR&교육콘텐츠를 키워드로 최근 3년(2018.1.1.~2020.12.31.)간 중앙지, 경제지 등 54개 언론사 기사를 빅카인즈와 데이터랩을 이용하여 관계도 분석, 월간 키워드 트렌드, 연관어 분석을 하였다. 'VR, AR, 에듀테크'를 키워드로 뉴스 검색결과 63,959건 중 '에듀테크' 검색결과 2018년 632건, 2019년 1043건, 2020년 2389건으로 해마다 급 상승하였다. '(AR+VR)AND 교육콘텐츠'에 대한 검색 결과 연관성(키워드 빈도수)이 높은 키워드로는 증강현실(120), 가상현실(116), 인공지능(114), 에듀테크(100), 코로나19(66), 실감형(65), 아이들(61), VR·AR(56), ICT(35), 빅데이터(25) 순으로 가상·증강현실 기술 발전, 코로나19의 장기화, 교육의 효율성으로 에듀테크 분야의 활용도가 급격히 증가함을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.72-74
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• 2000

인터넷을 효과적으로 검색하기 위하여 검색엔진을 많이 이용하고 있다. 그런데 문서의 키워드를 추출할 적에 지금까지는 Anchor Text를 염두에 두지 않았었다. Anchor Text는 사람이 직접 요약한 것이고(요약성), 하이퍼링크를 포함하는 웹 문서에 반드시 존재하므로(보편성) 그 하이퍼링크가 가리키는 곳의 문서의 키워드를 추출에 적합한 용도가 될 수 있다. 웹 그래프는 이러한 Anchor Text를 이용하여 키워드를 추출함으로써 문서와 문서간, 단어와 단어간의 관계(연관성)까지도 나타내 줄 수 있게 한 검색 엔진 시스템이다. 그러나 Anchor Text 자체가 본문의 내용이 아니고, Anchor Text를 작성한 사람에 따라 다르게 작성되며, 본문의 내용과 무관한 내용도 작성할 수 있다. 따라서 Anchor Text 자체를 어떠한 여과 없이 문서의 키워드로 받아들이긴 힘들다. 본 논문에서는 TFIDF를 통해 좀 더 정확성이 있는 키워드를 추출하였다.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.101-126
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• 2016

This paper identifies core research topics and their relationships by deriving the research topic networks in the technology management field using co-word analysis. Contrary to the conventional approach in which undirected networks are constructed based on normalized co-occurrence frequency, this study analyzes directed networks of keywords by employing the confidence index of association rule mining for pairs of keywords. Author keywords included in 2,456 articles published in nine international journals of technology management in 2011~2014 are extracted and categorized into three types: THEME, METHOD, and FIELD. One-mode networks for each type of keywords are constructed to identify core research keywords and their interrelationships with each type. We then derive the two-mode networks composed of different two types of keywords, THEME-METHOD and THEME-FIELD, to explore which methods or fields are frequently employed or studied for each theme. The findings of this study are expected to be fruitfully referred for researchers in the field of technology management to grasp research trends and set the future research directions.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1419-1425
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• 2017

The ultimate goal of legal knowledge search is to obtain optimal legal information based on laws and precedent. Text mining research is actively being undertaken to meet the needs of efficient retrieval from large scale data. A typical method is to use a word embedding algorithm based on Neural Net. This paper demonstrates how to search relevant information, applying Korean law information to word embedding. First, we extracts reference laws from precedents in order and takes reference laws as input of Law2Vec. The model learns a law by predicting its surrounding context law. The algorithm then moves over each law in the corpus and repeats the training step. After the training finished, we could infer the relationship between the laws via the embedding method. The search performance was evaluated based on precision and the recall rate which are computed from how closely the results are associated to the search terms. The test result proved that what this paper proposes is much more useful compared to existing systems utilizing only keyword search when it comes to extracting related laws.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.247-254
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• 2021

As COVID-19 spread, non-face-to-face activities were required, and the use of service robots is gradually increasing. This paper analyzed the relationship between the increasing trend of service robots before and after COVID-19 through keyword search containing the keyword 'service robot AND non-face-to-face' over the past three years (2018.10-20219) using BigKines, a news big data analysis system. As a result, there were 0 cases in the first period (2018.10~2019.9), 52 cases in the second period (2019.10~2020.9) and 112 cases in the third period (2020.10~2021.9), an increase of 115% compared to the second period. The keywords commonly mentioned in the analysis of related words in the second and third periods were COVID-19, AI, the Ministry of Trade, Industry, and Energy, and LG Electronics, and the weight of COVID-19 was the largest, confirming that the analysis keyword. Due to the spread of Corona 19, non-face-to-face is required, and with the development of information and communication technology, the field of application of service robots is rapidly increasing. Accordingly, for the commercialization of service robots that will lead the non-face-to-face economy, there is an urgent need to nurture human resources that require standardization and expertise in safety and performance fields.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.421-431
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• 2018

The purpose of this study is to analyze the trends of 'traditional' terminology in Korea. We focus on an empirical investigation of how media reports are conveying 'tradition' terminology in our society by applying text mining and social network analysis techniques. The analysis covered 2,481,143 news articles related to 'tradition' terminology that appeared in the media since the 1920's. In this research, frequency analysis, association analysis and social network analysis were used on articles related to 'tradition' terminology from 1920 to 2017 by decade. By applying these data science techniques, we can grasp the meaning of social culture phenomenon related 'tradition' with objective and value-neutral position and understand the social symbolism which contains the tradition of the times.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.596-598
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• 2010

본 논문에서는 의료영상 중 X-ray 영상을 대상으로 영상을 분류하고 분류 결과에 따라 다중 키워드를 생성하는 방법을 제시한다. X-ray영상은 대부분 그레이 영상임으로 Local Binary Patterns (LBP)을 이용하여 픽셀간의 연관성을 특징으로 추출하고, 실시간 학습 및 분류가 가능한 Random Forests 분류기로 영상들을 30개의 클래스로 분류한다. 또한, 미리 정의된 신체 부위간의 관계 가중치를 분류 스코어에 결합하여 신뢰값을 생성하고 이를 기반으로 영상에 대해 다중 주석을 부여하게 된다. 이렇게 부여된 다중 주석은 키워드 기반의 의료영상을 가능케 함으로 보다 쉽고 효율적인 검색 환경을 제공할 수 있다.

• 한국벤처창업학회:학술대회논문집
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• 2018.11a
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• pp.93-96
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• 2018

통계청 자료에 따르면 지난해 자영업 폐업률은 전년 대비 10.2% 포인트 높은 87.9%를 기록했다. 자영업 4개 업종인 도소매업, 음식, 숙박업은 지난해 48만 3985개가 새로 생겼으며 반면 42만 5203개가 문을 닫았다. 2018년 8월 통계청 자료에 따르면 온라인쇼핑 거래액은 전년 동월 대비 19.6% 증가했고, 모바일쇼핑 거래액도 모바일 이용 확산과 간편 결제 서비스 발전 등에 힘입어 전년 동월 대비 29.7% 증가하였다. 본 연구에서는 소매업인 온라인 쇼핑몰은 매년 20%대의 성장률을 보이고 있는 점에 주목해서 온라인 쇼핑몰의 매출 성과와 연관이 있는 방문 의도에 미치는 변수를 연구하고자 한다. 해마다 판매자가 증가하고 있는 온라인 쇼핑몰 업계는 치열한 경쟁과 늘어나는 마케팅 비용을 감당하지 못해 폐업의 수순을 밟고 있는 경우가 많다. 온라인쇼핑 운영자들이 하고 있는 대표적인 마케팅 방법인 블로그, 키워드 검색광고, SNS 채널 마케팅이 온라인 쇼핑몰의 방문 의도에 얼마나 영향을 미치고 있는지 연구 조사하고자 한다. 특히 온라인 쇼핑몰에 방문하는 사람들의 개인적 특성인 충동구매 성향, 다양성 추구 성향, 자기조절 성향을 조절 변수로 하여 개인 성향에 따라 각 독립변수들과의 방문 의도 상관관계를 알아보려 한다. 독립변수들이 방문 의도와의 관계를 실증 분석하여 온라인쇼핑몰의 준비하는 예비창업자나 기창업자들에게 보다 지속적인 기업성장과 매출성과에 도움이 되고자 한다.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.63-85
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• 2018

The "About Us" page on the website of a corporation provides information regarding the organization's vision, philosophy, and values. We examine the association between institutional information provided on corporate websites (i.e., the "About Us" section) with present and future financial performance. Utilizing a text mining technique, we analyze the institutional information of S&P500 firms in the year 2016. We conduct a factor analysis including words that are intentionally repeated in the introductory text of corporate websites. The results of the analysis reveal that keywords from this institutional information can be grouped into six factors. We then carry out an ordinary least squares regression analysis to determine the associations between these six factors and present financial performance. The results show that keywords in Factor 2 (those related to Purchasing experience) are positively associated with ROE, a variable representing present financial performance, while keywords in Factor 1 (those related to Note to customers) show a negative relationship with ROE. On the other hand, keywords in Factor 1 have a positive relationship with Tobin's Q, a variable representing future financial performance. These results indicate that there is some relationship between the words used in the institutional information in this section of corporate websites and firms' financial performance. Hence, the institutional information on a website may be a useful indicator of current firm performance and future firm value.