• Journal of Korean Library and Information Science Society
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• v.51 no.2
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• pp.1-21
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• 2020

The purpose of this study is to explore semantic characteristics of IFLA school library guidelines through network analysis. There are two versions, 2002 edition and 2015 revision of the guidelines. This study analyzed the 2002 edition and 2015 revision of the IFLA school library guidelines view point of semantic network, and compared characteristics of two versions. The keywords were to extracted from two texts, semantic network were composed based on co-occurrence relations with keywords. The centrality(degree centrality, closeness centrality, betweenness centrality) was analyzed from the network. In addition, this study conducted topic modeling analysis using LDA function of NetMiner4.0. The result of this study is following these. First, When comparing the centrality, the 'Program, Teaching, Reading, Inquiry, Literacy, Media' keyword was higher in the 2015 revision than in the 2002 edition. Second, 'Inquiry' in degree centrality and 'Achievement' in closeness centrality which were not included in the 2002 edition top-ranked keyword list, have new appeared in 2015 revision. third, As a result of the analysis of topic modeling, compared to the 2002 version, the importance of topics on programs and services, teaching and learning activities of librarian teacher, and media and information literacy is increasing in the 2015 revision.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.

• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.1-13
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• 2021

The purpose of this study is to identify trends and potential themes of research on consumption of children and adolescents for 20 years by analyzing keywords. The keywords of 869 studies on consumption of children and adolescents published in journals listed in Korean Citation Index were analyzed using text mining techniques. The most frequent keywords were found in the order of youth, youth consumers, consumer education, conspicuous consumption, consumption behavior, and character. As a result of analyzing the frequency of keywords by dividing into five-year periods, it was confirmed that the frequency of consumer education was significantly higher betwn 2006 and 2010. Research on ethical consumption has been active since 2011, and research has been conducted on various topics instead of without a prominent keyword during the most recent 5-year period. Looking at the keywords based on the TF-IDF, the keywords related to the environment and the Internet were the main keywords between 2001 and 2005. From 2006 to 2010, the TF-IDF values of media use, advertisement education, and Internet items were high. From 2011 to 2015, fair trade, green growth, green consumption, North Korean defector youths, social media, and from 2016 to 2020, text mining, sustainable development education, maker education, and the 2015 revised curriculum appeared as important themes. As a result of topic modeling, eight topics were derived: consumer education, mass media/peer culture, rational consumption, Hallyu/cultural industry, consumer competency, economic education, teaching and learning method, and eco-friendly/ethical consumption. As a result of network analysis, it was found that conspicuous consumption and consumer education are important topics in consumption research of children and adolescents.

• Journal of East-West Nursing Research
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• v.26 no.2
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• pp.118-129
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• 2020

Purpose: The purpose of this study was to analyze the topic trend of domestic simulation nursing education research using text network analysis(TNA). Methods: This study was conducted in four steps. TNA was performed using the NetMiner (version 4.4.1) program. Firstly, 245 articles from 4 databases (RISS, KCI, KISS, DBpia) published from 2008 to 2018, were collected. Secondly, keyword-forms were unified and representative words were selected. Thirdly, co-occurrence matrices of keywords with a frequency of 2 or higher were generated. Finally, social network-related measures-indices of degree centrality and betweenness centrality-were obtained. The topic trend over time was visualized as a sociogram and presented. Results: 178 author keywords were extracted. Keywords with high degree centrality were "Nursing student", "Clinical competency", "Knowledge", "Critical thinking", "Communication", and "Problem-solving ability." Keywords with high betweenness centrality were "CPR", "Knowledge", "Attitude", "Self-efficacy", "Performance ability", and "Nurse." Over time, the topic trends on simulation nursing education have diversified. For example, topics such as "Neonatal nursing", "Obstetric nursing", "Pediatric nursing", "Blood transfusion", "Community visit nursing", and "Core basic nursing skill" appeared. The core-topics that emerged only recently (2017-2018) were "High-fidelity", "Heart arrest", "Clinical judgment", "Reflection", "Core basic nursing skill." Conclusion: Although simulation nursing education research has been increasing, it is necessary to continue studies on integrated simulation learning designs based on various nursing settings. Additionally, in simulation nursing education, research is required not only on learner-centered educational outcomes, but also factors that influence educational outcomes from the perspective of the instructors.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.67-78
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• 1999

Information providers on WWW have been rapidly increasing, and they provide a vast amount of information in various fields, Because of this reason, it becomes hard for users to get the information they want. Although there are several search engines that help users with the keyword matching methods, it is not easy to find suitable keywords. In order to solve these problems with a specific domain, we propose an intelligent information agent(HHA-la : HongIk Information Agent) that converts user's q queries to forms including related domain words in order to represent user's intention as much as it can and provides the necessary information of the domain to users. HHA-la h has an ontological knowledge base of animal domain, supplies necessary information for queries from users and other agents, and provides relevant web page information. One of system components is a WebDB which indexes web pages relevant to the animal domain. The system also supplies new operators by which users can represent their thought more clearly, and has a learning mechanism using accumulated results and user feedback to behave more intelligently, We implement the system and show the effectiveness of the information agent by presenting experiment results in this paper.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• Journal of Engineering Education Research
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• v.17 no.4
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• pp.43-47
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• 2014

The rapid development of science & technology and the globalization of society have accelerated the fractionation and specialization of academic disciplines. Accordingly, Korean colleges and universities are continually dropping antiquated courses to make room for new courses that better meet societal demands. With emphasis placed on providing students with a broader range of choices in terms of course selection, compulsory courses have given way to elective courses. On average, 4 year institutions of higher learning in Korea currently offer somewhere in the neighborhood of 1,000 different courses yearly. The classification of an ever growing list of courses offered and the practical use of such data would not be possible without the aid of computers. For example, if we were able to show the pre/post requisite relationship among various courses as well as the commonalities in substance among courses, such data generated regarding the interrelationship of different courses would undoubtedly greatly benefit the students, as well as the professors, during course registration. Furthermore, the GT system's relatively simple approach to course classification and coding will obviate the need for the development of a more complicated keyword based search engine, and hopefully contribute to the standardization of the course coding scheme in the future..Therefore, as a sample case project, this study will use GT to classify and code all courses offered at the College of Engineering of K University, thereby developing a system that will facilitate the scanning of relevant courses.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.18-33
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• 2014

A Word is the smallest unit for text analysis, and the premise behind most text-mining algorithms is that the words in given documents can be perfectly recognized. However, the newly coined words, spelling and spacing errors, and domain adaptation problems make it difficult to recognize words correctly. To make matters worse, obtaining a sufficient amount of training data that can be used in any situation is not only unrealistic but also inefficient. Therefore, an automatical word extraction method which does not require a training process is desperately needed. WordRank, the most widely used unsupervised word extraction algorithm for Chinese and Japanese, shows a poor word extraction performance in Korean due to different language structures. In this paper, we first discuss why WordRank has a poor performance in Korean, and propose a customized WordRank algorithm for Korean, named KR-WordRank, by considering its linguistic characteristics and by improving the robustness to noise in text documents. Experiment results show that the performance of KR-WordRank is significantly better than that of the original WordRank in Korean. In addition, it is found that not only can our proposed algorithm extract proper words but also identify candidate keywords for an effective document summarization.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.467-474
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• 2018

To organize and document the contents of meetings and discussions is very important in various tasks. However, in the past, people had to manually organize the contents themselves. In this paper, we describe the development of a system that generates the meeting minutes automatically using the TextRank algorithm. The proposed system records all the utterances of the speaker in real time and calculates the similarity based on the appearance frequency of the sentences. Then, to create the meeting minutes, it extracts important words or phrases through a non-supervised learning algorithm for finding the relation between the sentences in the document data. Especially, we improved the performance by introducing the keyword weighting technique for the TextRank algorithm which reconfigured the PageRank algorithm to fit words and sentences.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.211-218
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• 2008

In this paper, we proposed a new image summarization algorithm designed for automatically summarizing user's snapshot photos taken in a virtual environment based on user's context information and educational contents, and then presenting a summarized photos shortly after user's virtual reality experience. While other image summarization algorithms used date, location, and keyword to effectively summarize a large amount of photos, this algorithm is intended to improve users' memory retention by recalling their interests and important educational contents. This paper first describes some criteria of extracting the meaningful images to improve learning effects and the identification rate calculations, followed by the system architecture that integrates the virtual environment and the viewer interface. It will also discuss a user study to model the algorithm's optimal identification rate and then future research directions.