• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.117-120
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• 2001

In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and then choose a number of terms called initial representative keywords (IRKS) from them through fuzzy inference. Then, by expanding and reweighting IRKS using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKS so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The results show that our approach outperforms the other approaches.

• The Journal of Information Technology
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• v.1 no.1
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• pp.13-26
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• 1998

In general Information retrieval systems, improper keywords are often extracted and different search results are offered comparing to user's aim bacause the systems use only term frequency informations for selecting keywords and don't consider their meanings. It represents that improving precision is limited without considering semantics of keywords because recall ratio and precision have inverse proportion relation. In this paper, a system which is able to improve precision without decreasing recall ratio is designed and implemented, as client user module is introduced which can send feedbacks to server with user's intention. For this purpose, keywords are selected using relative term frequency and inverse document frequency and co-occurrence words are extracted from original documents. Then, the keywords are clustered by their semantics using calculated mutual informations. In this paper, the system can reject inappropriate documents using segmented semantic informations according to feedbacks from client user module. Consequently precision of the system is improved without decreasing recall ratio.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.1
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• pp.253-275
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• 2022

Niklas Luhmann is one of the most contentious and difficult theorist in sociology but follow-up studies on his theory gradually increase for recent 10 years. The purpose of this study is to observe how follow-up studies use the difficult concepts of Luhmann. Unlike previous studies, this study adopted a keyword rather than an article as the unit of analysis because keywords are linguistic constructs that can make concepts observable. The study analyzed co-occurrence of keywords in 139 articles retrieved from social sciences category in Web of Science DB. The key findings were following: the most important keywords were the name of Luhmann(Niklas Luhmann) and theory(social systems); keywords were grouped into 4 clusters(social systems theory, systems theory, legal system and political system, the significant of Luhmann's theory from the viewpoint of the history of social theory); topic terms were systems theory, communication, Autopoiesis, risk, legal system, functional differentiation, environment, social theory, sociological theory, structural coupling, systems and evolution. The significance of the study is following: the study gives keywords as useful access point for beginners of Luhmann's theory; the study proves that content analysis by keywords network can be applied to trend analysis of difficult theoretical researches.

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.50-58
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• 2016

Purpose: The purpose of this study was to identify the knowledge structure of cancer survivors. Methods: For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords. Results: According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'. Conclusion: Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

• Perspectives in Nursing Science
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• v.16 no.1
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• pp.12-24
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• 2019

Purpose: This study examined the suitability of using text network analysis (TNA) methodology for topic analysis of academic papers related to nursing. Methods: TNA background theories, software programs, and research processes have been described in this paper. Additionally, the research methodology that applied TNA to the topic analysis of the academic nursing papers was analyzed. Results: As background theories for the study, we explained information theory, word co-occurrence analysis, graph theory, network theory, and social network analysis. The TNA procedure was described as follows: 1) collection of academic articles, 2) text extraction, 3) preprocessing, 4) generation of word co-occurrence matrices, 5) social network analysis, and 6) interpretation and discussion. Conclusion: TNA using author-keywords has several advantages. It can utilize recognized terms such as MeSH headings or terms chosen by professionals, and it saves time and effort. Additionally, the study emphasizes the necessity of developing a sophisticated research design that explores nursing research trends in a multidimensional method by applying TNA methodology.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.101-126
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• 2016

This paper identifies core research topics and their relationships by deriving the research topic networks in the technology management field using co-word analysis. Contrary to the conventional approach in which undirected networks are constructed based on normalized co-occurrence frequency, this study analyzes directed networks of keywords by employing the confidence index of association rule mining for pairs of keywords. Author keywords included in 2,456 articles published in nine international journals of technology management in 2011~2014 are extracted and categorized into three types: THEME, METHOD, and FIELD. One-mode networks for each type of keywords are constructed to identify core research keywords and their interrelationships with each type. We then derive the two-mode networks composed of different two types of keywords, THEME-METHOD and THEME-FIELD, to explore which methods or fields are frequently employed or studied for each theme. The findings of this study are expected to be fruitfully referred for researchers in the field of technology management to grasp research trends and set the future research directions.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.538-549
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• 2019

Purpose: This study aimed to explore and compare the knowledge structure of pain management nursing research, between Korea and other countries, applying a text network analysis. Methods: 321 Korean and 6,685 international study abstracts of pain management, published from 2004 to 2017, were collected. Keywords and meaningful morphemes from the abstracts were analyzed and refined, and their co-occurrence matrix was generated. Two networks of 140 and 424 keywords, respectively, of domestic and international studies were analyzed using NetMiner 4.3 software for degree centrality, closeness centrality, betweenness centrality, and eigenvector community analysis. Results: In both Korean and international studies, the most important, core-keywords were "pain," "patient," "pain management," "registered nurses," "care," "cancer," "need," "analgesia," "assessment," and "surgery." While some keywords like "education," "knowledge," and "patient-controlled analgesia" found to be important in Korean studies; "treatment," "hospice palliative care," and "children" were critical keywords in international studies. Three common sub-topic groups found in Korean and international studies were "pain and accompanying symptoms," "target groups of pain management," and "RNs' performance of pain management." It is only in recent years (2016~17), that keywords such as "performance," "attitude," "depression," and "sleep" have become more important in Korean studies than, while keywords such as "assessment," "intervention," "analgesia," and "chronic pain" have become important in international studies. Conclusion: It is suggested that Korean pain-management researchers should expand their concerns to children and adolescents, the elderly, patients with chronic pain, patients in diverse healthcare settings, and patients' use of opioid analgesia. Moreover, researchers need to approach pain-management with a quality of life perspective rather than a mere focus on individual symptoms.

• Journal of Korean Library and Information Science Society
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• v.42 no.4
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• pp.353-374
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• 2011

Analyzing research paper published from 2005 to 2010, this study aims for analysing the research paradigm on multi-culture and understanding the structural characteristics of the multicultural knowledge via scientometric. Co-word network constructed by keywords in documents and their co-occurrence relationships is a kind of mapping knowledge structure. A total of 4,521 and 1,373 papers published between 2005 and 2010 were retrieved from the KRF Registered Journals and Proposed Journals. This paper employs k-core analysis method in the field of mapping knowledge structure to analyze keyword co-occurrence network of multicultural research in Korea. And Netminer 3 is employed to visualize the networks in this paper.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.461-468
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• 2020

This study examined how national research and development (R&D) in the domain of logistics has changed recently in the Republic of Korea. We conducted basic statistical analysis and social network analysis on 5,327 logistics-related R&D projects undertaken during 2005-2019. Data for performing these analyses were collected from the R&D database of the National Science and Technology Information Service (NTIS). By constructing a co-occurrence matrix with keywords, we conducted degree and betweenness centrality analysis and visualized the network matrix to display a cluster map. This study presents our observations related to the following findings: (1) the chronical trends of logistics R&D, (2) focused fields of logistics R&D, (3) the relations among keywords, and (4) the characteristics of logistics R&D. Finally, we suggest policy implications to boost and diversify logistics R&D.