• Journal of Korean Society of Archives and Records Management
• /
• v.23 no.4
• /
• pp.73-89
• /
• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• Journal of the Korea Convergence Society
• /
• v.9 no.9
• /
• pp.199-207
• /
• 2018

This study aims to analyze Next Production Revolution (NPR) technologies through evidence-based keyword network in order to cope with the change of production paradigm called the Fourth Industrial Revolution (4IR). For the analysis, a total of 441 papers related to NPR or 4IR were extracted and the NPR technology network was constructed based on the simultaneous appearance relationship of the author keywords of these papers. Based on the NPR technology network, we explored key technologies through analysis of centrality and keyword group. As a result, technologies such as 'digital twin' and 'modeling and simulation', discovering insights by connecting the virtual and physical world in real time and reflecting them into design and process, are analyzed as key technologies.

• Journal of Cadastre & Land InformatiX
• /
• v.49 no.2
• /
• pp.67-82
• /
• 2019

The purpose of this study is to analyze the issues and perceptions of smart cities. First, based on the big data analysis platform, big data analysis on smart cities were conducted to derive keywords by year, word cloud, and frequency of generation of smart city keywords by time. Second, trend and flow by area were analyzed by reclassifying major keywords by year based on meta-keywords. Third, emotional recognition flow for smart cities and major emotional keywords were derived. While U-City in the past is mostly centered on creating infrastructure for new towns, recent smart cities are focusing on sustainable urban construction led by citizens, according to the analysis. In addition, it was analyzed that while infrastructure, service, and technology were emphasized in the past, management and methodology were emphasized recently, and positive perception of smart cities was growing. The study could be used as basic data for the past, present and future of smart cities in Korea at a time when smart city services are being built across the country.

• Journal of the Korean Society for Library and Information Science
• /
• v.57 no.3
• /
• pp.351-379
• /
• 2023

The goal of this study is to investigate the domestic and international research trends on metaverse related researches. To achieve this goal, a set of 913 journal articles were collected from KCI (Korea Citation Index), 232 articles from WoS (Web of Science), and 277 articles from WoS-CPCI (Conference Proceeding Citation Index). A descriptive analysis shows the number of researches has been increased radically, and the mostly researched subject areas are interdisciplinary, computer science, and education in KCI, business and economics in WoS, and computer science in WoS-CPCI. The co-occurrence network analysis using author keywords revealed that technology related terms such as virtual reality and augmented reality showed high centrality measures in all of the databases, and the cluster analysis resulted in education and metaverse platform related keywords cluster from KCI, bibliometric analysis related keywords cluster from WoS, and all the metaverse technology related keywords cluster from WoS-CPCI.

• The KIPS Transactions:PartB
• /
• v.19B no.2
• /
• pp.149-154
• /
• 2012

The aim of this paper is to collect relevant keywords from clicklog data including user's keywords and URLs accessed using them. Our main hyphothesis is that two or more different keywords may be relevant if users access same URLs using them. Also, they should have higher relationship when the more same URLs are accessed using them. To validate our idea, we collect relevant keywords from clicklog data which is offered by a portal site. As a result, our experiment shows 89.32% precision when we define answer set to only semantically same words, and 99.03% when we define answer set to broader sense. Our approach has merits that it is independent on language and collects relevant words from real world data.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.07a
• /
• pp.177-178
• /
• 2019

본 연구는 지방교육자치의 가장 핵심인 '교육감' 관련 연구들의 주요 핵심어들과 그들 간의 관계성을 분석하였다. 본 연구에서는 2009년부터 2018년까지(10년간)의 '교육감' 관련 선행연구 총 93건을 키워드 네트워크 분석 방법론을 활용하여, 주요 핵심어 추출 및 워드 클라우드 제시, 주요 핵심어들 간의 관계성(의미망 네트워크) 분석 등을 진행하였다. 최근 10년간 국내 '교육감' 관련 연구들의 주요 핵심어들은 교육감선거, 주민직선제, 선출제도, 개선방안, 비교연구, 교육자치, 문제점, 지방자치, 교육부장관, 교육위원 등 이었다. 주요 핵심어들(상위 출현빈도)은 높은 밀도와 연결정도를 가지고 상호 네트워크를 형성하고 있었다. 본 연구결과는 향후 진행될 '교육감' 관련 후속연구들의 새로운 연구주제 선정 및 다양한 방향 설정에 기초자료로 활용될 수 있을 것이다.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.37-43
• /
• 2022

In this paper, we calculate the word frequency by date and region using news data related to COVID-19 distributed for about 8 months from December 2019 to July 2020, and visualized the correlation with the current state data of COVID-19 patients using the results. News data was collected from Big Kids, a news big data system operated by the Korea Press Promotion Foundation. The visualization system proposed in this paper shows the news frequency of the selected region compared to the overall region, the key keyword of the selected region, the region of the main keyword, and the date change of the selected region. Through this visualization, the main keywords and trends of COVID-19 confirmed and infected people can be identified for previous events.

• Spatial Information Research
• /
• v.23 no.1
• /
• pp.69-77
• /
• 2015

The aim of this study is to examine the research trends of global scientific production of Geospatial Information (GI) papers from 1998 to 2013 by using keyword network analysis. This study constructed keyword network model through papers and keywords related to GI research retrieved from the Web of Science DB and performed keyword network analysis such as Degree Centrality, Betweenness Centrality, and Closeness Centrality. The results show that GI has been steadily applied to various fields, and also the research trends of GI techniques could be quantitatively characterized through keyword network analysis. This study result can be applied to establish the policies and the national R&D planning of geospatial information.

• Journal of the Korean Society for information Management
• /
• v.35 no.1
• /
• pp.231-250
• /
• 2018

In this paper, we propose a new methodology for extracting and formalizing subjective topics at a specific time using a set of keywords extracted automatically from online news articles. To do this, we first extracted a set of keywords by applying TF-IDF methods selected by a series of comparative experiments on various statistical weighting schemes that can measure the importance of individual words in a large set of texts. In order to effectively calculate the semantic relation between extracted keywords, a set of word embedding vectors was constructed by using about 1,000,000 news articles collected separately. Individual keywords extracted were quantified in the form of numerical vectors and clustered by K-means algorithm. As a result of qualitative in-depth analysis of each keyword cluster finally obtained, we witnessed that most of the clusters were evaluated as appropriate topics with sufficient semantic concentration for us to easily assign labels to them.

• The Journal of the Korea Contents Association
• /
• v.22 no.7
• /
• pp.1-9
• /
• 2022

Today, artificial intelligence (AI) has emerged as a key driver of national competitiveness, but it is also causing unexpected side effects in society. This study intends to examine major social issues by collecting papers on AI targeting KCI journals in the field of social science. Therefore, we conducted keyword analysis of papers from 2016 to 2020. As a result of the analysis, the keywords for 'robot' and 'education' appeared the most, and the top six clusters (issues) were derived through the keyword network. The main issues are as follows: the background and/or basic concept of AI, AI education, side effects of AI, legal issues of AI-based creations, intention to use AI products/services, and AI ethics. The results of this study can be used to expand the discussion on the social aspects of AI and to find policy directions at the national level.