• Journal of Korean Library and Information Science Society
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• v.48 no.1
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• pp.207-225
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• 2017

The aim of this study was to discover various Library and Information Science (LIS) research areas by examining similarities and differences between LIS journals in terms of keyword characteristics. To conduct this study, for the years from 2004 to 2016, the keywords of 6 international journals were downloaded from Scopus database (http://www.scopus.com), and the keywords of 4 Korean journals were downloaded from the RISS database (http://www.riss.co.kr). The characteristics of keywords were investigated by examining frequently used keywords and frequently used distinctive keywords pertaining to international and Korean journals. The distinctive keywords are referred to as the keywords that appear in one domain but not in another. The result of this study indicated the following: a) a frequency analysis of the keywords showed major research themes and unique traits concerning Korea. b) In general, the keywords used in Korean journals frequently reflected the library as a major subject area of research, while keywords used in international journals reflected bibliometrics and information retrieval as major subject areas of research. c) The overarching themes of each created dataset were clearly noticeable in frequently used distinctive keywords. d) Some keywords were bound by a nation or by a region due to their scope of usage. The important implication of this study is that both most frequently used keywords and most frequently used distinctive keywords seemed to adequately represent the LIS subject areas.

• The Journal of Information Systems
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• v.28 no.4
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• pp.271-291
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• 2019

Purpose The purpose of this study is to propose a methodology to derive positive keywords and negative keywords through deep learning to classify reviews into positive reviews and negative ones, and then refine the results of topic modeling using these keywords. Design/methodology/approach In this study, we extracted topic keywords by performing LDA-based topic modeling. At the same time, we performed attention-based deep learning to identify positive and negative keywords. Finally, we refined the topic keywords using these keywords as filters. Findings We collected and analyzed about 6,000 English reviews of Gyeongbokgung, a representative tourist attraction in Korea, from Tripadvisor, a representative travel site. Experimental results show that the proposed methodology properly identifies positive and negative keywords describing major topics.

• Journal of Korean society of Dental Hygiene
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• v.23 no.6
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• pp.451-458
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• 2023

Objectives: The purpose of this study is to explore the research trends of elderly oral health through a language network analysis. Methods: A total of 354 published studies with 668 keywords were collected from the Research Information Sharing Service (RISS) between 2000 and 2022. Language network analysis was performed using Textom 6.0, Ucinet 6.774, and NetDraw 2.183. Results: The most frequent keywords were 'elderly', 'oral health', 'quality of life', and 'OHIP-14'. The result of frequency-inverse document frequent keywords showed similar results to the most frequent keywords. The N-gram of keywords shows that 'elderly', 'oral health' (18 times) and 'elderly', 'depression' (7 times). As a results of the analysis of degree centrality and between centrality, 'elderly', 'oral health', and 'quality of life' were found to be high. The CONCOR analysis identified the main clusters of 'quality of life', 'oral health behavior', 'health', and 'oral function disorder'. Conclusions: The results of the current study could be available to know research trends in elderly oral health and it is necessary to improve more comprehensive study in follow-up study.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.4
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• pp.691-707
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• 2020

This study used big data analysis as informatics that identified keywords related to new-tro fashion; in addition, it conducted differences and types of classification according to demographic characteristics. First, it has been shown that two different generations, the Millennials and the older generation, coexist as important keywords in the context of new-tro fashion. Second, according to age, it has been shown that the keywords that appear in new-tro fashion are taken differently. In most regional keywords that differed in the classification, respondents in their 20s, 30s and 40s were classified as emotional, while those in their 50s or older perceived as factual phenomena. The results of eliciting keywords in new-tro fashion through big data analysis, keywords that reflect phenomena, design details and considerations, fashion styles, fashion brands, fashion items, social media, influence, and emotional adjectives. This study confirmed the meaning of new-tro fashion based on past that can give enjoyment to the new generation and memories to the older generation.

• Research in Community and Public Health Nursing
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• v.29 no.4
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• pp.429-439
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• 2018

Purpose: The aim of this study is to identify core keyword of frailty research in the past 35 years to understand the structure of knowledge of frailty. Methods: 10,367 frailty articles published between 1981 and April 2016 were retrieved from Web of Science. Keywords from these articles were extracted using Bibexcel and social network analysis was conducted with the occurrence network using NetMiner program. Results: The top five keywords with a high frequency of occurrence include 'disability', 'nursing home', 'sarcopenia', 'exercise', and 'dementia'. Keywords were classified by subheadings of MeSH and the majority of them were included under the healthcare and physical dimensions. The degree centralities of the keywords were arranged in the order of 'long term care' (0.55), 'gait' (0.42), 'physical activity' (0.42), 'quality of life' (0.42), and 'physical performance' (0.38). The betweenness centralities of the keywords were listed in the order of depression' (0.32), 'quality of life' (0.28), 'home care' (0.28), 'geriatric assessment' (0.28), and 'fall' (0.27). The cluster analysis shows that the frailty research field is divided into seven clusters: aging, sarcopenia, inflammation, mortality, frailty index, older people, and physical activity. Conclusion: After reviewing previous research in the 35 years, it has been found that only physical frailty and frailty related to medicine have been emphasized. Further research in psychological, cognitive, social, and environmental frailty is needed to understand frailty in a multifaceted and integrative manner.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.17-27
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• 2018

In this paper, the research trends of port hinterland from 1990 to 2018 were analyzed periodically using the Social Network Analysis (SNA) method. The data were collected from major academic journals and totally 116 papers were identified for analysis. The results of the analysis showed that in the first period (1990-1999), keywords can be listed as "containerization", "transport infrastructure" and developed countries related keywords like "Italy", "Canada" and "Germany". The results of the second period (2000-2009) were originated from keywords such as "regionalization", "competitiveness", "Asian consolidation" and "technology". In the third period (2010-2018), the results were derived from keywords such as "intermodal transport", "dry port", "container" and container related keywords and "shipping" and shipping related keywords. We could see the studies of port hinterland are becoming more systematic and integrated. This study provides some important implications for both academic, and industrial viewpoints, and it is helpful to understand the research concentration.

• 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.

• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.393-414
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• 2023

The goal of this study is to investigate domestic research trend on research data study. To achieve this goal, articles related research data topic were collected from RISS. After data cleansing, 134 author keywords were extracted from a total of 58 articles and keyword network analysis was performed. As a result, first, the number of studies related to research data in Korea is still only 58, so it was found that many related studies need to be conducted in the future. Second, most research fields related to research data were focused on library and information science among complex studies. Third, as a result of frequency analysis of author keywords related to research data, 'research data management', 'research data sharing', 'data repository', and 'open science' were analyzed as major frequent keywords, so research data-related research focuses on the above keywords. The keyword network analysis results also showed that high-frequency keywords occupy a central position in degree centrality and betweenness centrality and are located as core keywords in related studies. Through the results of this study, we were able to identify trends related to recent research data and identify areas that require intensive research in the future.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.2
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• pp.203-220
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• 2016

This research analyzed characteristic changes in trading areas and the success factors of popular fashion trading areas (Garosu-gil, Dongdaemun, and Itaewon). This research adopts a social network analysis method to semantically analyze trading areas. Articles on the three fashion trading areas were located through KrKwic software to extract keywords and calculate word frequency. Keywords with high frequency were placed through NodeXL software to identify relationships among keywords. Researchers created a network of relationships among trading areas and between past and present of trading areas to analyze and visualize. In the past (2008-2009), the trading areas of Dongdaemun, Garosu-gil and Itaewon were placed horizontally with a limited number of shared keywords between them. However, the three trading areas retain diverse rage of keywords and an organic realtionships five years later (2013-2014). Compared to the past, all three areas see the emergence of relevant fashion keywords such as 'designer', 'design', 'brand'. Additional cultural keywords such as 'culture/art/performance', 'exhibition', and 'event' have commonly appeared and imply that related industries are an important factor as well. Fashion companies that consider evaluating areas for a new store opening need to understand the trading area characteristics and select the most suitable area. In addition, it is necessary to equip the trading area with basic fashion elements as well as relavant industry when the government tries to develop fashion trading areas.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.237-243
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• 2016

Since realtime search keywords of portal sites are arranged in descending order by instant increasing rates of search numbers, they easily show issues increasing in interests for a short time. But they have the limits extracted different results by portal sites and not shown issues by a period. Thus, to find key issues from the whole realtime search keywords for certain period, and to show results of summarizing them and analyzing differences, is significant in providing the basis of understanding issues more practically and in maintaining consistency of them. This paper analyzes differences of week key issues extracted from week analysis of realtime search keywords provided by two typical portal sites. The results of experiments show that the portal group means of realtime search keywords by the independent t-test and the survival functions of realtime search keywords by the survival analysis are statistically significant differences.