• Journal of Information Management
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• v.27 no.1
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• pp.1-39
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• 1996

The purpose of this paper is to design a model of integrated OPAC called as EconRef. This model not only provides users of libraries with systematic, rapid information service, but also supports librarians to do their tasks effectively. The designed model is constructed based on two operating systems such as REGIS system and The Book House and is developed by using KPWin++ is an expert system shell which combines hypertext and expert system functions. The designed system consists of six modules ; three reference expert systems for document sources, experts and statistical sources; OPAC ; external database ; user's guide. For the evaluation of the designed system, performance of EconRef system is compared with that of the naive and expert reference librarians. And also the features of the system are compared with those of REGIS systems. The tests comparing BconRef system searching with librarians searching have shown that EconRef system is at least as good as searching with expert librarians and much superior to searching with naive librarians.

• Journal for History of Mathematics
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• v.18 no.3
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• pp.91-106
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• 2005

History of mathematics must be analysed to discuss mathematical reality and thinking. Analysis of history of mathematics is the method of understanding mathematical activity, by these analysis can we know how historically mathematician' activity progress and mathematical concepts develop. In this respects, we investigate teaching algebra through historico-genetic analysis and propose historico-genetic analysis as alternative method to improve of teaching school algebra. First the necessity of historico-genetic analysis is discussed, and we think of epistemological obstacles through these analysis. Next we focus two concepts i.e. letters(unknowns) and negative numbers which is dealt with school algebra. To apply historico-genetic analysis to school algebra, some historical texts relating to letters and negative numbers is analysed, and mathematics educational discussions is followed with experimental researches.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.173-183
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• 2017

In this research, analysis based on VR journalism outlet the New York Times' was carried out. The image analysis of was done through the frames of angle, shot (size, length, movement), and limited user-directed interaction (point, sound). The result of this is as follows. Firstly, the direction was done using a basis of normal and low angles. Secondly, it was able to be confirmed that the shooting was done in order by medium, full, and long shot. Thirdly, with regard to the length of the shot, most direction was done through long takes. Fourthly, most images came to consist of fixed shots. Lastly, this is limited user-directed interaction. This may be separated into 2 aspects: sound, and movement of the independent free agent. Through these, interaction was guided through free point of view concerning realistic situations to point of view guidance and users. This research may be referred to as foundational research for the further advancement of in-depth discussion pertaining to VR journalism.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.67-78
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• 2019

The purpose of this study is to explore information ethics issues based on academic big data from Web of Science (WoS) and to provide implications for information ethics education in informatics subject. To this end, 318 published papers from WoS related to information ethics were text mined. Specifically, this paper analyzed the frequency of key-words(TF, DF, TF-IDF), information ethics issues using topic modeling, and frequency of appearances by year for each issue. This paper used 'tm', 'topicmodel' package of R for text mining. The main results are as follows. First, this paper confirmed that the words 'digital', 'student', 'software', and 'privacy' were the main key-words through TF-IDF. Second, the topic modeling analysis showed 8 issues such as 'Professional value', 'Cyber-bullying', 'AI and Social Impact' et al., and the proportion of 'Professional value' and 'Cyber-bullying' was relatively high. This study discussed the implications for information ethics education in Korea based on the results of this analysis.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.419-426
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• 2018

Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.67-77
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• 2021

This study conducted topic modeling, association analysis, and sentiment analysis focused on text mining in order to reflect regional characteristics in the process of establishing an information plan in Chungcheongbuk-do. As a result of the analysis, it was confirmed that Chungcheongbuk-do occupies a relatively high proportion of educational activities to bridge the information gap, and is interested in improving infrastructure to provide non-face-to-face, untouched administrative services, and bridge the gap between urban and rural areas. In addition, it is necessary to refer to the fact that there is a positive evaluation of the combination of bio and IT in the regional strategic industry and examples of ICT innovation services. It has been confirmed that smart cities have high expectations for the establishment of various cooperation systems with IT companies, but continuous crisis management is necessary so that they are not related to political issues. It is hoped that the results of this study can be used as one of the methods to specifically reflect regional changes in the process of informatization.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.60-66
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• 2021

The importance of maintenance is leading the application of the maintenance strategy in various industries. The maritime industry is also a part of them, with changes in selecting and applying the maintenance strategy, but rather slowly, by retaining the old strategy. In particular, the ship is maintaining a previously used strategy. In the circumstance of the sea, ship requires a new suggestion for maintenance strategy. A ship predictive maintenance model predicts the breakdown or malfunction of machineries to secure maintenance time with preventive actions and treatments, thereby avoiding maintenance-related dangerous factors. This study focused on applying text analysis to an Liquefied Natural Gas Carriers dock indent document, and the analysis results were interpreted from the original document. The inter-relational patterns observed from the frequency of common maintenance combinations among different parts and equipment in ships will be applied to the development of ship predictive maintenance.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.145-156
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• 2022

In recent years, as there is growing concern with ESG (Environmental, Social, and Governance), the strategic direction of business management is changing from maximizing shareholders wealth to maximizing stakeholders value. ESG is reshaping a corporation's supply chain management strategies. The purpose of this study is to explore the ESG challenges in supply chain management. As a result of network text analysis and topic modeling analysis on 3226 news articles, 'Suppliers', 'Sustainability', 'Shared Growth' 'Carbon Neutral', 'Safety and Health', 'Responsible Business Alliance', 'Supply Chain Due Diligence Law' were identified as the main issue. Since ESG initiatives in the supply chain are not limited to the efforts of individual firms, future research should focus on figuring out what difficulties and challenges exist in the diffusion of ESG practices along multi-tiered supply chains, and how to overcome them.