• Journal of Korean Library and Information Science Society
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• v.50 no.4
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• pp.339-358
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• 2019

The purpose of this study is to understand the topics of the research for the establishment of cooperative platform between libraries, archives, and museums that carry out the common task of providing knowledge information in a broad sense. To achieve the purpose of this study, 637 bibliographic information on three institutions were collected from the Web version of Scopus database. Among the collected bibliographic information, 5,218 words were extracted through NetMiner V.4 and analysed topic modeling. The results are as follows: First, as a result of analyzing the frequency of word appearance according to the tf-idf weight 'Preservation' was the most hottest topic. Second, the topic modeling analysis through LDA(Latent Dirichlet Allocation) algorithm resulted in 13 topic areas. Third, as a result of expressing 13 topic areas as a network, repository construction was the central topic, and the research topics such as cooperation among institutions, conservation environment for collections, system and policy discovery, life cycle of collections, exhibition of information resources, and information retrieval were closely related to the central topic. Fourth, the trend of 13 topic areas by year 1998 is limited to the specific subjects such as system and policy discovery, information retrieval, and life cycle of collections, while the subsequent studies have been carried out after that year.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.147-178
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• 2021

In this study, co-word analysis was conducted to investigate the intellectual structure of the domestic open access area. Through KCI and RISS, 124 research articles related to open access in Korea were selected for analysis, and a total of 1,157 keywords were extracted from the title and abstract. Network analysis was performed on the selected keywords. As a result, 3 domains and 20 clusters were extracted, and intellectual relations among keywords from open access area were visualized through PFnet. The centrality analysis of weighted networks was used to identify the core keywords in this area. Finally, 5 clusters from cluster analysis were displayed on a multidimensional scaling map, and the intellectual structure was proposed based on the correlation between keywords. The results of this study can visually identify and can be used as basic data for predicting the future direction of open access research in Korea.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.80-89
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• 2019

Prospecting future social environmental changes and improvement research on future technologies is required for prospecting promising technology, as it would be useful for institution·company to set up technical planning. This study aims at providing a methodology for retaining international technology competitiveness, marketable industry, and sustainable promising technology in a field of new growth engine industry such as national unmanned aerial vehicle industry. We draw a result by analysing with tools such as KrKwic, Excel, NetMiner, presenting methods of a Social Network Analysis, sub-group analysis, and cognitive map analysis based on patent data in a field of unmanned aerial vehicle industry. Therefore, this study explored the technology evolution of UAV and to prospect promising technology. As a result, some future promising technologies are prospected as what worths concentrated investment, such as 'system integration tech', 'assessment/airworthiness certification tech', 'avionics', 'pilot control tech', 'identification of friend or foe', 'flight control tech', 'supportive equipment'.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.59-71
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• 2020

The paper reports on a systematic literature review results concerning the smart factory research in Korea. 144 papers were identified from the articles published in Korean journals listed in the Korean citation index by keyword search related to smart factory. Bibliometric analyses were conducted by way of co-occurrence and network analysis using the VOSViewer. Automation, intelligence, and bigdata were identifed as three critical clusters of research while, operating systems, international policy and cases, concept analysis as other three clusters of research. Internet of Things turned out to be a key technology of smart factory linking all of these areas. Servitization studies were small in numbers but seemed to have a lot of potential. Security researches seemed to be lacking connections with other areas of studies. Results of this study can be used as a milestone for identifying future research issues in smart factories.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.275-294
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• 2019

In order to understand the recent trends in mathematics education research papers, data mining method was applied to analyze journals of the mathematics education posterior to the year of 2016. Text mining method is useful in the sense that it utilizes statistical approach to understand the linkages and influencing relationship between concepts and deriving the meaning that data shows by visualizing the process. Therefore, this research analyzed the key words largely mentioned in the recent mathematics education journals. Also the correlation between the subjects of mathematics education was deduced by using topic modeling. By using the trend analysis tool it is possible to understand the vital point which researchers consider it as important in recent mathematics education area and at the same time we tried to use it as a fundamental data to decide the upcoming research topic that is worth noticing.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.21-37
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• 2021

This study investigated the intellectual structure of the latest trends in Korean addiction research in the social sciences. A network analysis of keywords with co-word occurrence was performed on 172 papers from the KCI database based on the data from the year of 2019, and a total of 432 keywords were extracted. The network analysis was performed using several programs: Bibexcel, COOC, WNET, and NodeXL. As a result of the study, keywords related to addiction type, study subjects, research methods, and research variables were found, and a total of 20 clusters were identified. Furthermore, to identify and measure weighted networks, the relationships between each keyword were explored and discussed in detail through a network analysis of global centralities, local centralities, and betweenness centralities. The study indicated that the latest issues were focused on smartphone addiction and provided implications for the future research and practice that fields and topics of relationship addiction, food addiction, and work addiction should be more considered. Further, the study discussed the relationship between drug addiction-crime, alcohol addiction-family, and gambling addiction-motivation and the necessity of qualitative study.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.25-43
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• 2019

Recently there has been a growing interest in digital preservation and digital curation with rapid increase of digital resource. This study aims to investigate the research topics and the course topics related digital preservation and digital curation. The course information is collected from the curricular of library and information science departments and archival science departments in leading countries such as US, England, Ireland, Canada and New Zealand. Title keyword profiling and network analysis were adapted to discover core research and education areas. The key topics in the abstracts of research papers and the contents of the course were also illustrated by these methods. In the research analysis, archival system is the biggest area of researches related digital preservation and digital curation. Courser analysis shows digital curation education and process is the important area of education. As a result of content analysis, plan and strategy is a notable topic of research and record management process is a major topic of courses for digital preservation and digital curation. In addition, format of digital resource is an important topic for research and courses.

• Journal of the Korean School Mathematics Society
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• v.26 no.1
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• pp.1-19
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• 2023

This study analyzed the international trends of research concerning artificial intelligence in education by examining 352 papers recently published in the International Journal of Artificial Intelligence in Education(IJAIED) with the topic modeling method. The IJAIED is the official, SCOPUS-indexed journal of the International AIED Society. The analysis revealed that international AIED research trends could be categorized into eight topics with topics such as analyzing student behavior model in learning systems and designing feedback to student solutions being increased over time, whereas research focusing on data handling methods was decreased over time. Based on the findings implications and suggestions for the research and development of the applications of AIED were provided.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.333-337
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• 2017

본 논문에서는 개체명 인식과 언어 모델의 다중 학습을 이용한 한국어 개체명 인식 방법을 제안한다. 다중 학습은 1 개의 모델에서 2 개 이상의 작업을 동시에 분석하여 성능 향상을 기대할 수 있는 방법이지만, 이를 적용하기 위해서 말뭉치에 각 작업에 해당하는 태그가 부착되어야 하는 문제가 있다. 본 논문에서는 추가적인 태그 부착 없이 정보를 획득할 수 있는 언어 모델을 개체명 인식 작업과 결합하여 성능 향상을 이루고자 한다. 또한 단순한 형태소 입력의 한계를 극복하기 위해 입력 표상을 자소 및 형태소 품사의 임베딩으로 확장하였다. 기계 학습 방법은 순차적 레이블링에서 높은 성능을 제공하는 Bi-directional LSTM CRF 모델을 사용하였고, 실험 결과 언어 모델이 개체명 인식의 오류를 효과적으로 개선함을 확인하였다.