• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.173-180
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• 2018

In this paper, we analyzed key keywords and research themes in the field of defense research using keyword network analysis and tried to grasp the whole knowledge structure. To do this, we extracted data from 2,165 research data from defense related research institutes from 2010 to 2017 and applied the Pareto rule to the number of abstracts of words and the number of links between words, We extracted a total of 2,303 words based on the criterion and extracted 204 final key words through component analysis. By analyzing the centrality and cohesiveness through these key words, we confirmed the concept of core research in the defense field and derived a total of 7 large groups and 16 small groups of each group in the knowledge structure of the defense area.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.670-673
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• 2020

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.440-447
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• 2013

Recently corpus linguistic analyses enable researchers to examine meanings and structural features of data, that is not detected intuitively. While the potential of corpus linguistic techniques has been established and demonstrated for non-literary data, corpus stylistic analyses have been rarely performed in terms of the analysis of literature. Specifically this paper explores keywords and their role in text analysis, which is primary part of corpus linguistic analyses. This paper focuses on the application of techniques from corpus linguistics and the interpretation of results. This paper addresses the question of what is to be gained from keyword analysis by scrutinizing keywords in Shakespeare's Romeo and Juliet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.44-48
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• 2004

This paper proposes a phrase-based indexing system based on the phrase. the larger syntax unit than a single keyword. Early information retrieval systems with indexing system matching single keyword is simple and popular. But with single keyword matching it is very hard to represent the exact meaning of documents and the set of documents from retrieval is very large, therefore it can't satisfy the user of the information retrieval systems. Web documents include lots of syntactic errors, the natural language parser with high quality cannot be expected in Web. Partial trees, even not a full tree, from fully bottom-up parsing is still useful for extracting phrases, and they are much more discriminative than single keyword for index. It helps the information retrieval system enhance the efficiency and reduce the processing overhead, too.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• 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 Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.263-270
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• 2012

With the current development of high speed Internet and massive database technology, the amount of web documents increases rapidly, and thus, classifying those documents automatically is getting important. In this study, we propose an effective method to extract document features based on Hangeul morpheme and keyword analyses, and to classify non-structured documents automatically by predicting subjects of those documents. To extract document features, first, we select terms using a morpheme analyzer, form the keyword set based on term frequency and subject-discriminating power, and perform the scoring for each keyword using the discriminating power. Then, we generate the classification model by utilizing the commercial software that implements the decision tree, neural network, and SVM(support vector machine). Experimental results show that the proposed feature extraction method has achieved considerable performance, i.e., average precision 0.90 and recall 0.84 in case of the decision tree, in classifying the web documents by subjects.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.143-163
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• 2018

The business models has a great impact on the successful management of enterprises. Business environment has been shifting from industrial economy to knowledge-based economy. Enterprises go through numerous trials for successful management in the changing environment. Along with trial tests, research areas have been growing simultaneously. Although many researches have been conducted with regard to business models, it is very insufficient to systematically analyze the knowledge flow of research. Accordingly, successive researchers who want to study the business model may find it difficult to establish the orientation of future application research based on understanding the process of changing the knowledge structure that have accumulated so far. This study is intended to determine the current state of the business model research and to understand the process of knowledge structure changes in keywords that appear in 2,667 business model articles in the SCOPUS database. Identifying the knowledge structure has been completed through social network analysis, a methodology based on the 'relationship', and the changes in the knowledge structure were identified by classifying them into four different periods. The analysis showed that, first, the number of business model co-author increases over time with the need for academic diversity. Second, the 'innovation' keyword has the biggest center in the network, and over time, the lower-rank keyword which was in the former period has emerged as the top-rank keyword. Third, the cohesiveness group decreased from 12 before 2000 to 5 in 2015 and also the modularity decreased as well. Finally, examining characteristics of study area through a cognitive map showed that the relationships between domains increased gradually over time. The study has provided a systematic basis for understanding the current state of the business model research and the process of changing knowledge structure. In addition, considering that no research has ever systematically analyzed the knowledge structure accumulated by individual researches, it is considered as a significant study.