• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.71-86
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• 2021

This study was conducted to find information on the university's image from words related to 'Korea National College of Agriculture and Fisheries (KNCAF)' in Naver News. For this purpose, word frequency analysis, TF-IDF evaluation and semantic network analysis were performed using web crawling technology. In word frequency analysis, 'agriculture', 'education', 'support', 'farmer', 'youth', 'university', 'business', 'rural', 'CEO' were important words. In the TF-IDF evaluation, the key words were 'farmer', 'dron', 'agricultural and livestock food department', 'Jeonbuk', 'young farmer', 'agriculture', 'Chonju', 'university', 'device', 'spreading'. In the semantic network analysis, the Bigrams showed high correlations in the order of 'youth' - 'farmer', 'digital' - 'agriculture', 'farming' - 'settlement', 'agriculture' - 'rural', 'digital' - 'turnover'. As a result of evaluating the importance of keywords as five central index, 'agriculture' ranked first. And the keywords in the second place of the centrality index were 'farmers' (Cc, Cb), 'education' (Cd, Cp) and 'future' (Ce). The sperman's rank correlation coefficient by centrality index showed the most similar rank between Degree centrality and Pagerank centrality. The KNCAF articles of Naver News were used as important words such as 'agriculture', 'education', 'support', 'farmer', 'youth' in terms of word frequency. However, in the evaluation including document frequency, the words such as 'farmer', 'dron', 'Ministry of Agriculture, Food and Rural Affairs', 'Jeonbuk', and 'young farmers' were found to be key words. The centrality analysis considering the network connectivity between words was suitable for evaluation by Cd and Cp. And the words with strong centrality were 'agriculture', 'education', 'future', 'farmer', 'digital', 'support', 'utilization'.

• Journal of the Korean Society for information Management
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• v.34 no.3
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• pp.49-66
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• 2017

The aim of this study was to analyze the research trends on the digital libraries in Korea. To achieve this objective, a total of 578 author-assigned English keywords were collected from the 272 major LIS journal articles published in Korea during last ten years-period, i.e., 2007-2016. The collected data were analyzed using NetMiner V.4 to discover their 'degree centrality' and 'betweenness centrality'. As the results, 'Academic Library', 'Reference Service', 'Public Library', 'E Resource', and 'E Book' showed the most frequently conducted research topics, and 'Academic Library', 'Reference Service', 'Information Behavior', and 'E Resource' were the most influencing research topics. Finally, 'Academic Library', 'Metadata', 'Information Behavior', 'E Resource', and 'Librarian' seemed the most widely intervening research topics in this research.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.118-118
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• 2019

현재 홍수예보는 정형데이터인 유량 및 수위 등을 활용하여 이뤄지고 있다. 하지만 실제 사람들이 체감하는 홍수에 대한 위험도는 홍수예보 발령과는 달라 홍수예보가 이뤄지지 않은 지역에서 인명사고가 발생하기도 한다. 이는 수위 측정이 이뤄지지 않는 소규모 하천이나 사람들의 유동성이 큰 도심지역에서 빈번하게 발생한다. 이를 보완하기 위해서는 사람들의 체감 정도 및 인구의 유동성을 고려한 비정형데이터를 활용해야 한다. 특히 소셜 네트워크 서비스(Social Network Commuinty, SNS)를 사용하는 사람들이 많아지면서 기존에 사용되어 왔던 정형데이터 센서 이외의 데이터를 제공한다. 또한 개개인이 작성하는 글은 실시간으로 활용이 가능하여 인구의 유동성 및 시 공간적 데이터를 얻기에 유용하여 활용성이 매우 높은 비정형데이터이다. 따라서 본 연구에서는 SNS 데이터를 추출하고 이를 분석하여 2018년에 발생했던 강우사상과의 패턴을 비교하여 홍수예보에서의 활용성을 분석하였다. 홍수와 관련한 키워드를 중심으로 시 공간적 정보 및 추출이 가능한 웹 크롤러(Web Crawler) 프로그램을 작성하였으며 이를 토대로 데이터를 수집하였다. 수집한 데이터와 실제 홍수사상을 비교 분석을 한 결과 강우량 및 수위와 해당 지역에 대한 데이터의 양이 유사한 패턴을 보인 것으로 확인되었다. 실시간으로 데이터를 수집하고 이를 분석하여 리드타임을 충분히 확보한다면 홍수예측에 활용 가능할 것이라 생각된다. 본 연구는 한국건설기술연구원 19주요-대4-시드사업인 '커뮤니티 빅데이터 패턴 해석을 통한 수난(水難) 발생 및 규모 예측 기술 개발(20190126-001) '로 수행되었습니다.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.377-389
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• 2019

This study conducted social network big data analysis to investigate consumer's perception of Japanese sporting goods related to Japanese boycott and to extract problems and variables by recognition. Social network big data analysis was conducted in two areas, "Japanese boycott" and "Japanese sporting goods". Months of data were collected and investigated. If you specify the research method, you will identify the issues of the times - keyword setting using social network analysis - clustering using CONCOR analysis using TEXTOM and Ucinet 6 programs - variable selection through expert meetings - questionnaire preparation and answering - and validity of questionnaire Reliability Verification - It consists of hypothesis verification using the structural model equation. Based on the results of using the big data of social networks, four variables of relevant characteristics, nationality, attitude, and consumption behavior were extracted. A total of 30 questions and 292 questionnaires were used for final hypothesis verification. As a result of the analysis, first, the boycott-related characteristics showed a positive relationship with nationality. Specifically, all of the characteristics related to boycotts (necessary boycott, sense of boycott, and perceived boycott benefits were positively related to nationality. In addition, nationality was found to have a positive relationship with consumption behavior.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.122-135
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• 2024

The purpose of this study was to analyze the examples presented in the "Separation of Mixtures" section of the 2015 revised science authorized textbook introduced in elementary schools in 2022 and to see how the teachers and students understand the concept. To do that, 96 keywords were extracted through three cleansing processes to separate the elements of the mixture presented in the textbook. In order to analyze the teachers' perceptions, 32 teachers at elementary schools in Gyeonggi-do received responses to a survey, and a survey of 92 fourth graders who learned the separation of the mixture with an authorized textbook in 2022 was used for the analysis. As a result, as for the solids, 54 out of 96 separations (56.3%) showed the highest ratio, and the largest number of cases were presented according to the characteristics of the development stage of students. It was followed by living things, liquids, other objects and substances, and gasses. By analyzing the mixture, the structure and the interrelationships between the 96 extracted keywords were systematized through the network analysis, and the connection between the keywords, which were a part of the mixture was analyzed. The teachers partially responded to the separation of the complex mixture presented in the textbook, but most of the students did not recognize it. Because the analysis of the teachers' and students' perceptions of the seven separate categories presented in the survey was not based on a clear conceptual perception of the separation of the mixture, but rather they tended to respond differently for each characteristic of each individual category, it was decided that it was necessary to present clearer examples of the separation of the mixture, so that the students could better understand the concept of separation of mixtures that could be somewhat abstract.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.409-410
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• 2019

본 논문에서는 국내 갑상선암의 연구 동향을 파악하기 위해 텍스트 중심의 접근법을 제안한다. 국내 갑상선암은 2000년대에 들어서며 발생이 급증하여 과잉진단의 논란을 불러일으켰으나, 다양한 분야의 자정 노력으로 수술 환자수가 크게 줄었다. 본 연구에서는 텍스트 마이닝 기술을 사용하여 디비피아에 등록되어 있는 갑상선암 관련 논문의 키워드와 초록을 수집하여 분석하였다. 1980년대는 대부분의 사례보고가 있었고 1990년대에 들어서면서 검진을 통한 조기 진단의 내용이 자주 나타났다. 2000년대에는 여러 장비들을 활용한 검사방법과 미세한 암의 발견에 대한 논의가 증가하였음을 확인 할 수 있었다. 2010년대에 들어서는 환자의 삶의 질에 대한 연구가 많이 이루어졌다. 지난 수십 년 동안 갑상선 암 연구 주제에 대해 뚜렷한 변화가 나타났으며, 향후 연구의 기초자료로 활용될 수 있으리라 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.268-270
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• 2019

In this paper, we analyzed the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers. As a result of analysis, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been utilizing the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.1-28
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• 2017

Today, according to the increased importance of industry-university cooperation in the knowledge-based economy, support and the number of researches involved in industry-university cooperation has also steadily increased. But it is true that profits from the outcome of patents resulting from such cooperation, such as technology transfer and royalty fees, are lower than they are supposed to be, because of excessive patents applications, although some of them have little commercial potential. Therefore, this research aims to suggest a way to analyze and recognize patents, which enable efficient industry-university cooperation and technology transfer. For the analysis, data on 1,061 patents was collected from 4 different universities. With the data, a quality-strategy matrix was arranged targeting the industry-university cooperation foundations', US patents owned by universities, text mining, and social network analysis were carried out, particularly focusing on the patents in the advanced quality technology section of the matrix. Then core key words and IPC codes were obtained and key patents were analyzed by universities. As a result of the analysis, it was found that 4 key patents, 2 key IPC codes were drawn for University H, 4 key patents, 2 key IPC codes for University K, 6 key patents, 1 key IPC code for University Y, 14 key patents, and 2 key IPC codes for University S. This research is expected to have a great significance in contributing to the invigoration of industry-university cooperation based on the analysis result on patents and IPC codes, which enable efficient industry-university cooperation and technology transfer.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.11-39
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• 2018

The study examines the research trends of doctoral dissertations in Library Science and Information Science published in the U.S. for the last 5 years. Data collected from PQDT Global includes 1,016 doctoral dissertations containing "Library Science" or "Information Science" as subject headings, and keywords extracted from those dissertations were used for a network analysis, which helps identifying the intellectual structure of the dissertations. Also, the analysis using 103 subject heading keywords resulted in various centrality measures, including triangle betweenness centrality and nearest neighbor centrality, as well as 26 clusters of associated subject headings. The most frequently studied subjects include computer-related subjects, education-related subjects, and communication-related subjects, and a cluster with information science as the most central subject contains most of the computer-related keywords, while a cluster with library science as the most central subject contains many of the education-related keywords. Other related subjects include various user groups for user studies, and subjects related to information systems such as management, economics, geography, and biomedical engineering.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.35-46
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• 2016

Social network services (SNS) that help to build relationship network and share a particular interest or activity freely according to their interests by posting comments, photos, videos,${\ldots}$ on online communities such as blogs have adopted and developed widely as a social phenomenon. Several researches have been done to explore the pattern and valuable information in social networks data via text mining such as opinion mining and semantic analysis. For improving the efficiency of text mining, keyword-based approach have been applied but most of researchers argued the limitations of the rules of Korean orthography. This research aims to construct a database of non-standard Korean words which are difficulty in data mining such abbreviations, slangs, strange expressions, emoticons in order to improve the limitations in keyword-based text mining techniques. Based on the study of subjective opinions about specific topics on blogs, this research extracted non-standard words that were found useful in text mining process.