• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1467-1475
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• 2013

In this paper, we investigate a mutual associative relation between keyword when we used keyword in the papers. We propose how to organize mutual association between the key keyword and sub keyword in the micro battery and energy harvesting.

• Korean Journal of Childcare and Education
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• v.20 no.1
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• pp.91-111
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• 2024

Objective: The purpose of this study is to explore trends in institutional evaluation research in early childhood education through keyword network analysis. This aims to understand trends in academic discourse on institutional evaluation and gain implications for follow-up research and related policy directions. Methods: A total of 6,629 keywords were extracted from 572 dissertations and journal articles published from January 2006 to October 2023 for the purpose of analyzing and visualizing the frequency and centrality of keywords, as well as the structural properties of keyword networks. The analysis and visualization were conducted using the TEXTOM, UCINET6, and NetDraw programs. Results: First, the number of institutional evaluation studies increased steadily from 2006 to 2010 and then decreased, with a higher frequency of studies on daycare centers compared to kindergartens. Second, the most frequently occurring keyword in the analysis was 'daycare center,' and the highest connection strength was found in the term 'daycare-center-evaluation.' Third, network analysis revealed that key terms for institutional evaluation research included 'evaluation certification,' 'recognition,' 'evaluation indicators,' 'teacher,' 'daycare center,' and 'kindergarten.' In the ego network analysis for each institution, 'parent' emerged as a highly ranked keyword. Conclusion/Implications: This study confirmed the perspectives of previous studies by revealing the structure of core concepts in early childhood education institution evaluation research, and provided implications for follow-up and direction of institution evaluation

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.4
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• pp.73-85
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• 2022

The purpose of this study is to conduct more practical subsequent research by identifying research areas through a systematic analysis of sustainable fashion design research trends. For this study, 117 journals domestic journals published between 2010 and 2020 were selected using the keyword, 'sustainable fashion'. With the research materials, six top keywords, 'zero waste', 'sustainability', 'eco-friendly', 'upcycling', 'recycling', and 'ethical', were derived. The research status was examined by year, keyword, keyword and year, and research topic. The analysis results are as follows. First, looking into the studies by year, it was found that research on sustainable fashion increased in general. Compared to 2010, the research tripled in 2020, and it was found to have increased steadily from 2018. Second, regarding the research by keyword, eco-friendly was the most common. It can be seen that research tended to focus on recycling or eco-friendliness before, but in later material design development was heading towards upcycling. Third, concerning the research by topic, case studies were found the most before, but research on design development tended to increase recently. Based on that, it is expected that the areas of sustainable fashion design that need more research will be investigated further.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.511-518
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• 2012

In this study, the trend in green technology was observed and the domain of the green technology area that will be actively studied in the future was found by establishing knowledge map in green technology area and comparing and analyzing green technology information in Korea and overseas in time series. For the purpose of this study, network analysis was conducted for the keyword of green technology information provided by green technology information portal site (www.gtnet.go.kr) operated by Korea Institute of Science and Technology Information. Network analysis was conducted using keyword, and change of study subject was found by dividing the analysis result into periods. In the result of network analysis on top 100 keywords from total English keyword, it was found that renewable energy related areas such as solar energy and biomass had high centrality. When the main keyword trend by year was studied, centrality of solar cell, nanotechnology, smart grid, and fuel cell were found to increase, showing that research and development in generation and use of renewable energy are actively made.

• Journal of the Korean Dietetic Association
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• v.22 no.4
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• pp.310-319
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• 2016

Limitation of a questionnaire survey which is widely used is time and money, limited numbers of participants, biased confidence interval and unreliable results. To overcome these, we performed tendency and network analysis of diet using big Data in Koreans. The keyword on diet were collected from the portal site Naver from January 1, 2015 until December 31, 2015 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. The results showed that diet menu appeared most frequently by N-gram analysis, even though exercise had the highest frequency by simple frequency analysis. In addition, keyword network analysis were categorized into four groups: diet group, exercise group, commercial diet program company group and commercial diet food group. The analysis of seasonality showed that subjects' interests in diet had increased steadily since February, 2015, although subjects were most interested indiet in July, these results suggest that the best strategies for weight loss are based on diet menu and starting diet before July. As people are especially sensitive to diet trends, researches are needed about annual analysis of big data.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.4
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• pp.33-37
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• 2022

Purpose: This study aims to understand the trend and central concept of domestic researches on laughter therapy. For the analysis, this study used total 72 theses verified by inputting the keyword 'laughter therapy' from 2007 to 2021. Research design, data and methodology: This study performed the development and analysis of keyword co-occurrence network, analyzed the types of researches through topic modeling, and verified the visualized word cloud and sociogram. The keyword data that was cleaned through preprocessing, was analyzed in the method of centrality analysis and topic modeling through the 1-mode matrix conversion process by using the NetMiner (version 4.4) Program. Results: The keywords that most appeared for last 14 years were laughter therapy, depression, the elderly, and stress. The five topics analyzed in thesis data from 2007 to 2021 were therapy, cognitive behavior, quality of life, stress, and the elderly. Conclusions: This study understood the flow and trend of research topics of domestic laughter therapy for last 14 years, and there should be continuous researches on laughter therapy, which reflects the flow of time in the future.

• Asia Marketing Journal
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• v.16 no.3
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• pp.23-33
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• 2014

The selection of keywords for bidding is a critical component of paid search advertising. When the number of possible keywords is enormous, it becomes difficult to choose the best keywords for advertising and then subsequently to assess their effect. To this end, we propose an ultrahigh dimensional keyword selection approach that not only reduces the dimension for selections, but also generates the top listed keywords for profits. An empirical analysis using a unique panel dataset from a large online clothes retailer that advertises on the largest search engine in China (i.e., Baidu) is presented to illustrate the usefulness of our approach.

• Informatization Policy
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• v.27 no.4
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• pp.24-46
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• 2020

This study proposes a way to timely forecast open government data (OGD) demand(i.e., OGD requests, search queries, etc.) by using keyword network analysis. According to the analysis results, most of the OGD belonging to the high-demand topics are provided by the domestic OGD portal(data.go.kr), while the OGD related to users' actual needs predicted through topic association analysis are rarely provided. This is because, when providing(or selecting) OGD, relevance to OGD topics takes precedence over relevance to users' OGD requests. The proposed keyword network analysis framework is expected to contribute to the establishment of OGD policies for public institutions in the future as it can quickly and easily forecast users' demand based on actual OGD requests.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.1-8
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• 2020

The use of text data in big data analytics has been increased. So, much research on methods for text data analysis has been performed. In this paper, we study Bayesian learning based on conjugate prior for analyzing keyword data extracted from text big data. Bayesian statistics provides learning process for updating parameters when new data is added to existing data. This is an efficient process in big data environment, because a large amount of data is created and added over time in big data platform. In order to show the performance and applicability of proposed method, we carry out a case study by analyzing the keyword data from real patent document data.