• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.15-18
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• 2000

In this paper, we propose a method for text extraction in the Web images. Our approach is based on contrast detecting and pixel component ratio analysis in mouse position. Extracted data with OCR can be used for real time dictionary call or language translation application in Web browser.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.528-530
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• 2010

본 논문에서는 음악의 분석, 특히 대중가요의 가사 분석에 있어서 사회학적 관점인 기호학을 기초로 분석을 하였으며, 특히 '강산에'의 노래가사를 중심으로 분석을 하였다. 2집은 1994년에 발매 되었는데, 당시의 사회적 분위기와 맞물린 '비판적'가사의 흐름이외에도 '정서적', '의식적'인 구조에 대해서도 분석하였다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.655-667
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• 2014

This paper has identified five major categories of the T2D patients' concerns based on an online forum where the patients voluntarily verbalized their naturally occurring emotional reactions and concerns related to T2D. We have emphasized the fact that the lexical and phrasal analysis brought to the forefront the prevailing negative reactions and desires for clear information, professional advice, and emotional support. This study used lexical and phrasal analysis based on text-mining tools to estimate the potential of using a large sample of patient conversation of a specific disease posted on the internet for clinical features and patients' emotions. As a result, the study showed that quantitative analysis based on text-mining is a viable method of generalizing the psychological concerns and features of T2D patients.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.37-48
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• 2020

This study aimed at understanding trends of living lab studies and deriving implications for directions of the studies by utilizing text mining. The study included network analysis and topic modelling based on keywords and abstracts from total 166 thesis published between 2011 and November 2019. Centrality analysis showed that living lab studies had been conducted focusing on keywords like innovation, society, technology, development, user and so on. From the topic modelling, 5 topics such as "regional innovation and user support", "social policy program of government", "smart city platform building", "technology innovation model of company" and "participation in system transformation" were extracted. Since the foundation of KNoLL in 2017, the diversification of living lab study subjects has been made. Quantitative analysis using text mining provides useful results for development of living lab studies.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.23-30
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• 2020

This study used the text mining method to analyze the research trend of the Journal of Creative Information Culture(JCIC) which is the journal of convergence. The existing research trend analysis method has a limitation in that the researcher's personality is reflected using the traditional content analysis method. In order to complement the limitations of existing research trend analysis, this study used topic modeling. The English abstract of the paper was analyzed from 2015 to 2019 of the JCIC. As a result, the word that appeared most in the JCIC was "education," and eight research topics were drawn. The derived subjects were analyzed by educational subject, educational evaluation, learner's competence, software education and maker culture, information education and computer education, future education, creativity, teaching and learning methods. This study is meaningful in that it analyzes the research trend of the JCIC using text mining.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.1-15
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• 2022

In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.65-70
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• 2019

The purpose of this study is to understand the college students' needs for software curriculum which based on surveys from educational satisfaction of the software lecture evaluation, as well as to find out the improvement plan by applying the text content analysis method. The research method used the text content analysis program to calculate the frequency of words occurrence, key words selection, co-occurrence frequency of key words, and analyzed the text center and network analysis by using the network analysis program. As a result of this research, the decent points of the software education network are mentioned with 'lecturer' is the most frequently occurrence after then with 'kindness', 'student', 'explanation', 'coding'. The network analysis of the shortage points has been the most mention of 'lecture', 'wish to', 'student', 'lecturer', 'assignment', 'coding', 'difficult', and 'announcement' which are mentioned together. The comprehensive network analysis of both good and shortage points has compared among key words, we can figure out difference among the key words: for example, 'group activity or task', 'assignment', 'difficulty on level of lecture', and 'thinking about lecturer'. Also, from this difference, we can provide that the lack of proper role of individual staff at group activities, difficult and excessive tasks, awareness of the difficulty and necessity of software education, lack of instructor's teaching method and feedback. Therefore, it is necessary to examine not only how the grouping of software education (activities) and giving assignments (or tasks), but also how carried out group activities and tasks and monitored about the contents of lectures, teaching methods, the ratio of practice and design thinking.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• The Korean Journal of Emergency Medical Services
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• v.24 no.1
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• pp.85-92
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• 2020

Purpose: The purpose of this study was to analyze the English abstract key words found within the Korea Society of Emergency Medical Services journal using text mining techniques to determine the adherence of these terms with Medical Subject Headings (MeSH) and identify key word trends. Methods: We analyzed 212 papers that were published from 2012 to 2019. R software, web scraping, and frequency analysis of key words were conducted using R's basic and text mining packages. Additionally, the Word Clouds package was used for visualization. Results: The average number of key words used per study was 3.9. Word cloud visualization revealed that CPR was most prominent in the first half and emergency medical technician was most frequently used during the second half. There were a total of 542 (64.9%) words that exactly matched the MeSH listed words. A total of 293 (35%) key words did not match MeSH listed words. Conclusion: Researchers should obey submission rules. Further, journals should update their respective submission rules. MeSH key words that are frequently cited should be suggested for use.

• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.162-172
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• 1998

In this paper, we implement a gigabyte Hangul text retrieval engine HMG(Hangul MG) which is based on the English text retrieval engine MG(Managing Gigabytes) and the Hangul lexical analyzer HAM(Hangul Analysis Module). To support Hangul information, we use the KSC 5601 code in the database construction and query processing stages. The lexical analyzer, parser, and index construction module of the MG system are modified to support Hangul information. To show the usefulness of HMG system, we implemented a NOD(Novel On Demand) system supporting the retrieval of Hangul novels on the WWW. The proposed system HMG can be utilized in the construction of massive full-text information retrieval systems supporting Hangul.