• Journal of Korea Society of Industrial Information Systems
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• 제13권2호
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• pp.47-55
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• 2008

The purpose of this paper is to develop an analytical methodology of or릴 documents by the application of. Information Technologies. This system consists of the key word search, contents summary, clustering, classification & topic tracing of the contents. The integrated model of the five levels of retrieval technologies can be exhaustively used in the analysis of oral documents, which were collected as oral history of five men and women in the area of North Jeolla. Of the five methods topic tracing is the most pioneering accomplishment both home and abroad. In final this research will shed light on the methodological and theoretical studies of oral history and culture.

• Journal of Information Science Theory and Practice
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• 제11권3호
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• pp.29-42
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• 2023

This study explored issues related to the library in the COVID-19 era in YouTube videos in Korea. This study performed social network analysis and topic modeling analysis by collecting 479 YouTube videos, 20,545 words, and 8,379 channels related to COVID-19 and the library from 2019 to 2020. The study results confirmed that YouTube, a social media platform, was used as an important medium to connect users and physical libraries and provide/promote online library services. In the study, major topics and keywords such as quarantine, vlog, and library identity during the COVID-19 pandemic, library services and functions, and introductions and user guides of libraries were derived. Additionally, it was identified that videos about COVID-19 and the library are being produced by various actors (news and media channels, libraries, government agencies, librarians, and individual users). However, the study also identified that the actor network is fragmented through the channel network, showing a low density or weak linkage, and that the centrality of the library in the actor network is weak.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제21권2호
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• pp.85-100
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• 2022

Recently, with the widespread expansion of convergence based on digital connectivity, the transportation and mobility fields are rapidly changing, and research related to this is also diversifying. This study aims to analyze the research trends in the mobility field and identify key research areas and topics. Topic modeling analysis has been proved as a useful approach for analyzing the research trends. The abstracts of 142 research papers concerning mobility from the Korean academic citation index were analyzed, derived 9 research topics and linked to 6 key elements of research framework. The result showed that 'Advanced vehicle and transportaion technology' and 'Linkage and integrated services among means for mobility' were most actively studied research fields. It also found that research on insurance, law, regulation for securing user's safety and conflict-resolving with the existing industry has been conducted.

• Journal of the Korea Convergence Society
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• 제12권4호
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• pp.31-42
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• 2021

The purpose of this study was to explore research topics and examine the trend in COVID19 related research papers. We identified eight topics using latent Dirichlet allocation and found acceptable validity in comparison with the structural topic model. The subtopics have been extracted using k-means clustering and plotted in PCA space. Additionally, we discovered the topics bearing negative tones and warning signs by sentiment analysis. The results flagged up the issues of the topics, Biomedical Related, International Dynamics and Psychological Impact. The findings could serve as a guideline for researchers who explore new research directions and policymakers who need to make decisions about which research projects to support.

• Journal of Information Technology Applications and Management
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• 제22권3호
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• pp.83-103
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• 2015

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining-extracting meaningful information from unstructured text data-has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

• Journal of Digital Convergence
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• 제15권6호
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• pp.415-423
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• 2017

This study explored research trends by investigating articles published in the Journal of Korean Society of Fashion Design from 2001 through 2015. English key words and abstracts were analyzed using text mining and topic modeling techniques. The findings are as followings. By the text mining technique, 183 core terms, appeared more than 30 times, were derived from 7137 words used in total 338 articles' key words and abstracts. 'Fashion' and 'design' showed the highest frequency rate. After that, the well-received topic modeling technique, LDA, was applied to the collected data sets. Several distinct sub-research domains strongly tied with the previous fashion design field, except for topics such as fashion brand marketing and digital technology, were extracted. It was observed that there are the growing and declining trends in the research topics. Based on findings, implication, limitation, and future research questions were presented.

• The Journal of the Convergence on Culture Technology
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• 제7권2호
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• pp.367-375
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• 2021

This study aimed to identify the knowledge structure of researches on 'untact' and derived implications for directions for the studies using text mining. The study included network analysis and topic modelling of keywords and abstracts from 171 thesis published until October 2020. Centrality analysis showed that 'untact' studies had been focused on service, usage, consumption, technology and online. From the topic modelling, 6 topics such as 'COVID-19 and socio-technological change', 'needs and utilization of education contents', 'technology and service for user convenience', 'product marketing and sales', 'service design of the company', 'influence factors of usage and consumption' were extracted. Keywords that connect each topic were technology, service, usage, consumption, needs and factor. Exploratory analysis of 'untact' researches using text mining provides useful results for development of 'untact' studies.

• Journal of Convergence for Information Technology
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• 제12권3호
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• pp.25-37
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• 2022

The purpose of this study is to comprehensively check trends in policies, discourses, educational directions and contents, and social issues by deriving the subjective characteristics of peace education and unification education based on big data analysis. The results of this study are as follows. First, 'peace', 'unification', 'education', 'research', 'student', 'school', 'teacher', 'target', and 'Korean Peninsula' were commonly important keywords in peace education and unification education. Second, the top topic of peace education was 'peace education and civic education', and the top topic of unification education was ' sympathy and participation in unification education'. Third, topics that show an upward trend by regime in peace education were 'World Peace and Human Rights' and 'Object and Direction of Peace Education', and 'Subject of Unification Education' as topics that showed an upward trend by regime in unification education. Fourth, in peace education, the centrality of 'peace', 'education', 'student', 'school', and 'peace education' was high, and in unification education, 'unification', 'education', 'unification', 'school', and 'teacher' were high. Based on these results, it was intended to expand the horizon of understanding peace education and unification education, and to provide meaningful implications for establishing policies and conducting follow-up studies.

• International Journal of Contents
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• 제4권3호
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• pp.16-19
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• 2008

The purpose of this paper is to attempt the analysis of the 'Positivist' School and the 'Interpretative' School to the Relationship between Theory and Method, Using examples from Criminological Research. The differences between approach of the positivist school and interpretative school are often made very difficult to make an approach. What are more to focus on are the relationships between quantitative research and positivism and qualitative research and interpretative paradigms are often presented as essential connections. Therefore, it is a lot more difficult to imply an appropriate research method and process depends on the subject and field of the research topic. The differences between types of research and also the connections which are made between types of research and theoretical paradigms portray a very simple picture. The danger of this is that two readily available set of research methods and assuming that positivist and interpretative research style represent competing approaches and also that they present 'either-or' alternatives as to strategies of research. For example, in some instances positivist and interpretative's methods do represent alternatives but in some case, positivist and interprativist each other can complement about each other's defects in terms of use of methodology. Though depending according to the specialist of the topic, it needs sensitivity to the potentials and the contributions of different styles of data and different methods of information collection and analysis to the criminological issues under test and to the theoretical questions being asked of them.

• Communications of Mathematical Education
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• 제33권3호
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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.