• Journal of the Korean Society for Library and Information Science
• /
• v.56 no.4
• /
• pp.431-453
• /
• 2022

Hate is a collective expression of exclusivity toward others and it is fostered and reproduced through false public perception. This study aims to explore the objects and issues of hate discussed in our society using text mining techniques. To this end, we collected 17,867 news data published from 1990 to 2020 and constructed a co-word network and cluster analysis. In order to derive an explicit co-word network highly related to hate, we carried out sentence split and extracted a total of 52,520 sentences containing the words 'hate', 'prejudice' and 'discrimination' in the preprocessing phase. As a result of analyzing the frequency of words in the collected news data, the subjects that appeared most frequently in relation to hate in our society were women, race, and sexual minorities, and the related issues were related laws and crimes. As a result of cluster analysis based on the co-word network, we found a total of six hate-related clusters. The largest cluster was 'genderphobic', accounting for 41.4% of the total, followed by 'sexual minority hatred' at 28.7%, 'racial hatred' at 15.1%, 'selective hatred' at 8.5%, 'political hatred' accounted for 5.7% and 'environmental hatred' accounted for 0.3%. In the discussion, we comprehensively extracted all specific hate target names from the collected news data, which were not specifically revealed as a result of the cluster analysis.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10c
• /
• pp.580-582
• /
• 2002

최근 인터넷 관련 기술의 급속한 발전으로 데이터, 음성, 화상 등의 다양한 멀티미디어 서비스가 통합된 개방형 네트워크로 진화되어지고 있다. 모든 미디어가 인터넷으로 수렴되는 NGN(Next Generation Network)으로 발전할 전망이다. 개방형 네트워크는 다양한 유무선 통합망의 융합화에 따른 통신망간의 간섭이 증가하고 네트워크 접속점 중심의 통신망간 접속구조가 확대되어 지금까지의 시스템 보안 위주의 단순한 보안 기술을 적용하기가 어려웠다. 따라서 네트워크 노드간을 효율적으로 보호하는 네트워크 중심의 보안 기술이 필요한 시점이다. 이에 본 논문은 IETF에서 제안한 텍스트 기반 응용 계층의 접속제어 프로토콜로 실시간 미디어 통신을 위한 차세대 인터넷 프로토콜로 주목받고 있는 SIP(Session Initiation Protocol) 시스템을 분석하고, SIP 기반의 보안 메커니즘인 IPSec에서의 확장 보안 메커니즘을 설계하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
• /
• pp.198-201
• /
• 2011

본 연구는 현재 널리 사용되고 있는 소셜네트워크 속에서 일반 사용자들이 의료 도메인의 전문가들과 쉽게 질문과 응답을 주고 받을 수 있게 해주는 서비스 개발을 위한 기초 연구로써, 사용자의 문서를 분석하여 질문을 추출해 내고 어떤 의료 도메인에 해당하는 질문인지 분류하는 연구이다. 한글로 구성된 문서 속에서 질문에 해당하는 형태소 분석 방법을 이용하야 질문을 추출을 한 다음 질문 속의 단어 들을 분석하여 KORLEX를 이용한 단어간의 관계성을 분석하여 도메인을 분류하는 작업을 거친다. 또한 본 연구는 텍스트마이닝 기법과 인공지능의 분류 기법을 응용하여 소셜네트워크 속에서 질문과 응답을 분석하여, 의료 도메인의 전문가들이 볼 수 있게 함으로써, 소셜네트워크를 이용한 양방향의 질의응답 서비스를 제공 한다. 이 같은 양방향 질의응답 서비스를 통해 헬스케어 및 의료 관리 서비스를 받을 수 있다. 본 논문은 소셜네트워크 상에서 사용자들이 올린 헬스케어에 관련된 질문들을 추출하고 분류해 주는 과정에 한정하여 진행된 결과를 기술한다.

• Journal of Digital Convergence
• /
• v.16 no.11
• /
• pp.1-15
• /
• 2018

The purpose of this study is to analyze bibliographic information of Busan and other cities' reports for urban development initiative and identify the strategic fields for future smart city plan. Text mining method is used in this study to extract keywords and identify the characteristics and patterns of information in urban development reports. As a result, in earlier stage, Busan city focused on service creation for industrial development but there are lack of discussions on the linkage of information systems with ICT technology. However, recent urban planning in Busan contained various contents related to integrated connections of infrastructure, ICT system, and operation management of city in the specific fields of traffic, tourism, welfare, port/logistics, culture/MICE. This results of study is expected to provide policy implications for planning the future urban initiatives of smart city development.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.468-488
• /
• 2018

The maker movement is a phenomenon of society and culture where people who make necessary things come together and share knowledge and experience through creativity. However, as the maker movement has grown rapidly over the past decade, there is still a lack of consensus for how far they will be viewed as a maker movement. We need to look at how the maker movement has changed so far in order to find the direction of development of the maker movement. This study analyzes the media articles using text-based big data analysis methodology to understand how the issue of the maker movement has changed in general media. In particular, we apply Keyword Network Analysis and DTM(Dynamic Topic Model) to analyze changes of interest according to time. The Keyword Network Analysis derives major keywords at the word level in order to analyze the evolution of the maker movement, and DTM helps to identify changes in interest in different areas of the maker movement at three levels: word, topic, and document. As a result, we identified major topics such as start-ups, makerspaces, and maker education, and the major keywords have changed from 3D printer and enterprise to education.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.9
• /
• pp.1199-1205
• /
• 2021

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Journal of Korea Game Society
• /
• v.18 no.6
• /
• pp.69-82
• /
• 2018

The need of high quality VR contents has been steadily raised in recent years. Therefore, this study investigated the user's interest factors of VR game which is receiving the most attention among VR contents. We used STEAM review data and applied Text mining and Network analysis to perform this research. As a result, it was possible to confirm 4 word clusters related VR game users. Each cluster is named by 'presence', 'first person view game', 'auditory factor' and 'interaction'. This study has its meaning. First, user related research would be very helpful to develop high quality VR game. Second, it confirms that review data of VR game users can be structured, analyzed and used.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.471-492
• /
• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of the International Relations & Interdisciplinary Education
• /
• v.4 no.1
• /
• pp.85-111
• /
• 2024

Since the release of the 2000 PISA results, Finland's education has consistently been regarded as a competitor or benchmark for South Korea's educational system. However, recent indicators of division, opposition, and discontent within our educational sphere suggest a considerable departure from Finland's ethos of happiness in education. Against this backdrop, this study aims to analyze the trends in Finnish education-related research appearing in Korean academic journals. Utilizing network text analysis, we examined 160 papers indexed in RISS with titles containing "Finland" and "education". Key findings are as follows. Firstly, research on Finnish education has been steadily increasing, albeit showing recent signs of decline. Secondly, the majority of research topics were micro-level, with literature review-based methodologies predominating. Thirdly, a minority of researchers accounted for one-third of the total research output. Fourthly, countries compared with Finland predominantly included neoliberal states such as Japan, the United States, the United Kingdom, Australia, and Singapore. Fifthly, research themes and subjects primarily focused on primary and secondary education, particularly in domains such as mathematics and science, influenced by PISA. Future research on Finnish education should transcend localized and fragmented areas of inquiry, undertaking comprehensive investigations into the processes and history of Finland's happiness-oriented education. Such endeavors are essential for deriving insights crucial for our learning. Particularly, consideration should be given to moving beyond literature-based methodologies, fostering international collaborative discussions facilitated online, and linking the Finnish education community with educators, parents, students, local councils, and governmental stakeholders to collectively discuss and research.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.9 no.6
• /
• pp.681-689
• /
• 2019

The 46th Davos Forum of the World Economic Forum (WEF) predicts the continued growth of the 4th industry in the future. Currently, the 4th industry is attracting attention in various academic and practical fields. As a core technology of the 4th industry, Big Data is regarded as a major resource to lead the 4th industrial revolution along with artificial intelligence. As the growing interest in Big Data, researches on it are actively being done. However, literature studies on existing Big Data are focused on qualitative research, and quantitative research is insufficient. Therefore, this study aims to analyze the big data research flow in MIS field and to make academic thirst for quantification. This study has collected 145 abstracts of big data papers published in major journals in MIS field and confirmed that a majority of papers are published in Decision Support Systems Journal. Text mining and text network analysis were performed only for DSS journals to eliminate bias. As a result of the analysis, it was found out that researches on combining big data in the management field between 2012 and 2014, and researches on system development and analysis method for using big data from 2015 to 2017 were conducted.