• 한국문헌정보학회지
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• 제44권2호
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• pp.285-308
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• 2010

이 연구는 텍스트 마이닝 기법을 활용하여 국내 주거학 분야의 지적 구조를 분석하고자 하였다. 주요 주제와 핵심 저자, 그리고 주제 간 관계를 파악하기 위한 통계적 처리 과정에서 주로 문헌 클러스터링 기법을 사용했던 기존 연구와 달리 이 연구에서는 저자 프로파일링과 요인분석 기법을 적용하였다. 텍스트 마이닝으로 생성된 지적 구조의 해석을 보완하고 지적 구조 자체에 대한 평가를 수행하기 위해서 주거학 분야 연구자 2인과 질적 면담을 실시하였다. 그 결과 텍스트 마이닝을 통해 생성된 지적 구조는 전통적인 주거학 분야의 지적 구조와는 다소 다른 시각에서 나름대로 타당한 주제 구분을 보여주는 것으로 평가되었다.

• International Journal of Contents
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• 제11권2호
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• pp.15-19
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• 2015

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.

• 지능정보연구
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• 제8권1호
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• pp.27-39
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• 2002

본 논문은 다양한 웹 이미지로부터 문자영역(text block)의 위치를 알아내고 문자영역을 추출하는 방법을 제안한다. 인터넷 사용자관점에서 볼 때, 웹 이미지에 포함되어 있는 문자정보는 중요한 정보이지만 최근까지 이 분야의 연구는 그리 활발하지 못했다. 본 연구에서 제안된 알고리즘은 문자의 경사방향(skew)과 문자의 크기나 폰트에 관한 사전 정보 없이 수행되어 질 수 있도록 제안되었다 폰트 스타일과 크기에 제약되지 않고 문자영역을 적합하게 추출하기 위해 유용한 에지 검출, 문자 클러스터링 영역으로 정의되는 문자의 고유한 특성을 위한 히스토그램을 사용하였다. 다수의 실험을 통하여 제안된 방법을 테스트하고 수용할 만한 결과를 도출했다.

• Perspectives in Nursing Science
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• 제16권1호
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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.

• 산업경영시스템학회지
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• 제43권3호
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• pp.112-121
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• 2020

Recently, as the internet usage is increasing, accordingly generated text data is also increasing. Because this text data on the internet includes users' comments, the text data on the Internet can help you get users' opinion more efficiently and effectively. The topic of text mining has been actively studied recently, but it primarily focuses on either the content analysis or various improving techniques mostly for the performance of target mining algorithms. The objective of this study is to propose a novel method of analyzing the user's requirements by utilizing the text-mining technique. To complement the existing survey techniques, this study seeks to present priorities together with efficient extraction of customer requirements from the text data. This study seeks to identify users' requirements, derive the priorities of requirements, and identify the detailed causes of high-priority requirements. The implications of this study are as follows. First, this study tried to overcome the limitations of traditional investigations such as surveys and VOCs through text mining of online text data. Second, decision makers can derive users' requirements and prioritize without having to analyze numerous text data manually. Third, user priorities can be derived on a quantitative basis.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.246-254
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• 2019

The purpose of this study is to analyze the issues related to urban regeneration project at the present time through surveys and interviews of participants in the urban regeneration leading project of Suncheon city. Most of the comments were related to business fragmentation and things that should be improved in the future. The text network technique is applied to the subject analysis using unstructured text data. As a result of the frequency of appearance and analysis of page rank centrality between words, words of 'parking', 'need', 'lack', 'region' and 'resident' appeared at the top, and the result of analyzing the mediation centrality of key words showed 'culture', 'Need', 'region', 'inflow' and 'lack' appeared at the top. In the network analysis, the most central words appeared, and many words appeared in the important position in the sentence. Text network analysis has provided timely results in terms of sustainability after completion of the Suncheon City Regeneration Leading Project..

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• 한국안전학회지
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• 제33권6호
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• 한국식생활문화학회지
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• 제35권1호
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• pp.65-78
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• 2020

The objective of this study was to review and evaluate the growing subject of food tourism research, and thus identify the trend of food tourism research. Using a Text mining technique, this paper discovered the trends of the literature on food tourism that was published from 2004 to 2018. The study reviewed 201 articles that include the words 'food' and 'tourism' in their abstracts in the KCI database. The Wordscloud analysis results presented that the research subjects were predominantly 'Festival', 'Region', 'Culture', 'Tourist', but there was a slight difference in frequency according to the time period. Based on the main path analysis, we extracted the meaningful paths between the cited references published domestically, resulting in a total of 12 networks from 2004 to 2018. The Text network analysis indicated that the words with high centrality showed similarities and differences in the food tourism literature according to the time period, displaying them in a sociogram, a visualization tool. This study has implications that it offers a new perspective of comprehending the overall flow of relevant research.

• 한국컴퓨터정보학회논문지
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• 제24권7호
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• pp.125-133
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• 2019

Because military weapons systems for special purposes are small and complex, they are not easy to maintain. Therefore, it is very important to maintain combat strength through quick maintenance in the event of a breakdown. In particular, naval ships are complex weapon systems equipped with various equipment, so other equipment must be considered for maintenance in the event of equipment failure, so that skilled maintenance personnel have a great influence on rapid maintenance. Therefore, in this paper, we analyzed maintenance data of defense equipment maintenance information system through text mining and social network analysis(SNA), and tried to identify the naval ship maintenance expertise. The defense equipment maintenance information system is a system that manages military equipment efficiently. In this study, the data(2,538cases) of some naval ship maintenance teams were analyzed. In detail, we examined the contents of main maintenance and maintenance personnel through text mining(word cloud, word network). Next, social network analysis(collaboration analysis, centrality analysis) was used to confirm the collaboration relationship between maintenance personnel and maintenance expertise. Finally, we compare the results of text mining and social network analysis(SNA) to find out appropriate methods for finding and finding naval ship maintenance expertise.