• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.179-189
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• 2016

This aim of this study is identify research trends and intellectual structure in the field of Korean journalism and communication studies. For this purpose, a semantic network analysis was employed to analyze keywords in the abstracts of published articles in the Korean Journal of Journalism and Communication Studies from 2005 to 2015. The results showed that "frame", "Twitter", "content analysis" and "social media" are among the most frequently used keywords in the abstracts during this period. With regards to degree and eigenvector centrality, "social capital", "trust" and "twitter" were among the highest. The findings of the periodic characteristics of research trends revealed that there are more studies that employ the traditional media effect theories including Uses and Gratification Theory, Agenda Setting Theory, and Framing Theory before the year of 2010 while those that focus on the specific new media such as smartphones and twitter after 2011. This study has implications in the sense that it can be used as guidelines for making a curriculum or establishing the research system for Korean journalism and communication studies in the future.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.11-20
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• 2018

The Q&A board is widely used as a means of communicating service enquiries, and the need for efficient management of the enquiry system has risen because certain questions are being repeatedly and frequently registered. This study aims to construct a student-centered FAQ, centered on the unstructured data posted on the university homepage's Q&A board. We extracted major keywords from 690 postings registered in the recent 3 years, and conducted the semantic network analysis to find the relationship between the keywords and the centrality analysis in order to carry out network visualization. The most central keywords found through the analysis, in order of centrality, were application, curriculum, credit point, completion, graduation, approval, period, major, portal, department. Also, the major keywords were classified into 8 groups of course, register, student life, scholarship, library, dormitory, IT and commute. If the most frequent questions are organized into these areas to form the FAQ, based on the results above, it is expected to contribute to user convenience and the efficiency of administration by simplifying the service enquiry process for repeated questions, as well as enabling smooth two-way communication among the members of the university.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.235-243
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• 2022

This paper aims to find promising export items for market expansion of defense export items. Germany, the UK, and France were selected as export target countries to obtain unstructured forecast data on weapons system acquisition plans for the next ten years by each country. Using the TF-IDF in text mining analysis, keywords that appeared frequently in data from three countries were derived. As a result of this paper, keywords for each country's major acquisition projects drawing. However, most of the derived keywords were related to mainstay weapon systems produced by domestic defense companies in each country. To discover promising export items from text mining, we proposed that the drawn keywords are distinguished as similar weapon systems. In addition, we assort the weapon systems that the three countries will get a plan to acquire commonly. As a result of this paper, it can be seen that the current promising export item is a weapon system related to the information system. Prioritizing overseas demands using key words can set clear market entry goals. In the case of domestic companies based on needs, it is possible to establish a specific entry strategy. Relevant organizations also can provide customized marketing support.

• Proceedings of the Korean Society for Language and Information Conference
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• 2001.06a
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• pp.95-96
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• 2001

문장에서 술어를 수식하는 대표적인 표현은 부사어이다. 부사어는 일반적으로 문장 구성에서 핵심적 역할을 하지 않는 첨가어(adjuncts)이기 때문에 핵심적인 역할을 하는 보충어(complements)에 비해 상대적으로 연구가 덜 이루어진 분야이다. 그렇지만 부사어는 피수식어의 의미를 구체화/한정하는 기능을 하기 때문에 정확한 의사소통을 위해서는 반드시 필요한 요소이며 어순, 호응(concord)등의 통사적 현상과도 밀접한 관련이 있다. 이 연구의 일차적 목적은 이러한 부사어를 통사적, 의미적 기준에 의해 정확하게 분류하고 그들의 분포제약을 밝히는 것이다. 그 다음으로, 부사어와 관련된 통사 현상으로, 부사어와 피수식어의 공 기(co-occurrence)관계 및 부사어와 술어 어미의 호응 관계에 대한 분석을 제공하려고 한다. 부사어는 통사적인 기준과 의미적인 기준으로 분류할 수 있다 (손남익 1995, 김경훈 1996, 임유종 1998). 통사적 기준으로는 단어나 구를 수식하는 성분부사와 문장을 수식하는 문장 부사로 나누는 방법과 위치에 대한 제약이 있느냐 없느냐에 따라 제약부사와 자유부사로 나 누는 방법이 있다. 이 두 통사론적 기준에 의해 분류되는 부사들은 서로 어떤 상관관계를 보이고 있는지 살펴 볼 것이다. 일반적으로 문장부사는 문두에 놓여야 한다는 위치적 제 약 이 있기 때문에 제약부사로 분류된다. 의미적 기준으로 부사어를 분류할 수도 있는데, 시간/ 공간 부사어, 양태/정도 부사어 등으로 나눌 수 있다. 의미적 기준에 의해 분류된 부사어는 통사적 기준에 의해 분류된 것들과는 어떤 상호 관련성을 맺고 있는지 살펴 볼 것이다. 일 반적으로 시간부사와 장소부사는 자유부사에 속하며 양태부사와 정도부사는 제약부사에 속 한다. 부사어와 피수식 요소와의 통사적 공기 관계 및 의미적 관계 그리고 그와 관련된 문 법 현상도 연구의 대상이 된다. 예를 들어, 자유로운 어순을 가진 부사들이지만 “＊순이는 빨리 과연 달린다”에서 볼 수 있는 종류의 분포적 특성을 알아 볼것이다 (심재기 1982, 송 철의 1989). 또한 “길이/＊길게 빛나다”와 “＊길이/길게 드리워졌다”와 같은 대조에서 나타나는 통사, 의미적 기능의 상관 관계 및 제약들의 상호 작용도 살펴 볼 것이다.

• Journal of Society of Occupational Therapy for the Aged and Dementia
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• v.12 no.2
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• pp.67-74
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• 2018

Objective : The purpose of this study is to quantitatively analyze the role of occupational therapy in long - term care insurance for the elderly using text mining, one of the big data analysis techniques. Method : For the analysis of newspaper articles, "Long - Term Care Insurance for the Elderly + Occupational Therapy for the Elderly" was collected after the period from 2007 to 208. Naver, which has a high share of the domestic search engine, utilized the database of Naver News by utilizing Textom, a web crawling tool. After collecting the article title and original text of 510 news data from the collection of the elderly long term care insurance + occupational therapy search, we analyzed the article frequency and key words by year. Result : In terms of the frequency of articles published by year, the number of articles published in 2015 and 2017 was the highest with 70 articles (13.7%), and the top 10 terms of the key word analysis showed the highest frequency of 'dementia' (344) In terms of key words, dementia, treatment, hospital, health, service, rehabilitation, facilities, institution, grade, elderly, professional, salary, industrial complex and people are related. Conclusion : In this study, it is meaningful that the textual mining technique was used to more objectively confirm the social needs and the role of the occupational therapist for the dementia and rehabilitation in the related key keywords based on the media reporting trend of the elderly long - term care insurance for 11 years. Based on the results of this study, future research should expand research field and period and supplement the research methodology through various analysis methods according to the year.

• Journal of Korea Technology Innovation Society
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• v.17 no.2
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• pp.334-354
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• 2014

This study aims to analyze the research trend of Korean medicine based on social network analysis. To do this, a dataset has been collected from KCI (Korea Citation Index) database. According to the results, we have identify the longitudinal trend of the number of papers, journals, organizations and key words in this field. Moreover, based on the nodes' centrality of co-author network, we have found a core journal (i.e. Korean Journal of Oriental Physiology and Pathology), a hub institution (i.e. Kyunghee university) and two main key words (i.e. anti-oxidation and acupuncture) in the research network. In conclusion, integrating field experts' tacit knowledge in Korean medicine studies with the results of the explicit social network analysis on the research trend, we put forward further policy implications with regard to R&D strategies in this field.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• Korean Journal of Comparative Education
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• v.28 no.1
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• pp.1-24
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• 2018

The objective of the article is to find the research trends and the main traits presented in the keywords on abstracts of research articles of "International Journal of Education Development" from 2002 to 2017. To do this, Text Network Analysis(TNA) was applied targeting 966 papers on the journal and the major research outcomes are as follows. First, the frequency analysis on the keywords showed that the keywords like Administration of education program, Schools and instruction, Regional public administration, Educational support service, Elementary education, and Elementary and secondary school were analyzed more than 100 times and also high in centrality degree. Second, the analysis results of the keywords presented in those research articles by development goal periods showed that several new keywords like Elementary education, Elementary and secondary school, Education quality, Secondary education, Educational planning have emerged frequently after SDGs and these keywords showed high in their centrality analysis. Third, the analysis on education level showed that the keywords like Elementary education, Administration of education program, School children were high in frequency and centrality degree in Elementary level. In secondary level, Schools and instruction, Administration of education program, Academic achievement were high, and in high level, college and university was high, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.344-347
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• 2010

최근 주목받고 있는 의미적 정보처리의 지식베이스인 온톨로지는 정형화된 표현을 통해 정확한 지식 처리와 추론관계를 명시해야 하기 때문에 온톨로지 확장에 대한 중요성 역시 강조되고 있다. 온톨로지 확장을 위한 기존의 방법들은 전문가를 통한 수작업 형태이거나 보편화된 사전이나 시소러스 집단의 분석을 통한 통계의 확률분포를 이용하는 반자동화된 방법들이 있다. 이에 본 논문에서는 Wikipedia에서 특정 도메인 문서들만을 수집한 후 중요문장 추출과정을 통해 해당 문서 내의 핵심어를 파악하여 이를 온톨로지의 개념 인식을 위한 정보로 활용할 수 있는 방안을 제시하고자 한다.