• 한국BIM학회 논문집
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• 제13권3호
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• pp.39-47
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• 2023

This study aims to introduce a model for enhancing community well-being through the utilization of public open data. To objectively assess abstract notions of residential satisfaction, text data from complaints is analyzed. By leveraging accessible public data, costs related to data collection are minimized. Initially, relevant text data containing civic complaints is collected and refined by removing extraneous information. This processed data is then combined with meaningful datasets and subjected to topic modeling, a text mining technique. The insights derived are visualized using Geographic Information System (GIS) and Application Programming Interface (API) data. The efficacy of this analytical model was demonstrated in the Godeok/Gangil area. The proposed methodology allows for comprehensive analysis across time, space, and categories. This flexible approach involves incorporating specific public open data as needed, all within the overarching framework.

• 산경연구논집
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• 제11권7호
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• pp.7-17
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• 2020

Purpose: The purpose of this study was to identify factors that enhance the effectiveness of bulk text message advertising on consumer attention in the telecommunications industry in Zimbabwe. Research design, data and methodology: The study collected data using structured questionnaires. The study attracted 293 responses from consumer subscribers of the Zimbabwean telecommunications industry. Data was analysed using SPSS and measures of association, direction, strength and significance were used. Results: The study found out that the examined variables of bulk text messaging (Simplicity, Frequency and Informativeness) had a positive significant impact on consumers' attention (β= 0.645; p-value < 0.05). The study examined four bulk text advertising determinants, namely frequency, simplicity, informativeness and credibility. Only credibility was found to be statistically insignificant (p-value > 0.05), whilst frequency had an inverse effect on consumer attention. Simplicity of bulk text advertisements recorded a high positive and significant impact whilst informativeness was also positively, and significantly affecting consumer attention. Conclusions: The study concluded that for bulk text advertising to be effective, text messages should be informative, easy to understand and dispatched less frequently. It was further concluded that bulk text advertising should follow permission marketing where consumers consent before hand to be recipients of commercials.

• Journal of Information Technology Applications and Management
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• 제26권3호
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• pp.43-59
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korean new year addresses, one of most important and authoritative document publicly announced by North Korean government. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. We propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and co-occurrence networks. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of Kim Jung Un of the North Korea from 2017 to 2019. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. We found that uncovered semantic structures of North Korean new year addresses closely follow major changes in North Korean government's positions toward their own people as well as outside audience such as USA and South Korea.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.789-816
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• 2019

Text classification is a challenging task, especially when dealing with a huge amount of text data. The performance of a classification model can be varied depending on what type of words contained in the document corpus and what type of features generated for classification. Aside from proposing a new modified version of the existing algorithm or creating a new algorithm, we attempt to modify the use of data. The classifier performance is usually affected by the quality of learning data as the classifier is built based on these training data. We assume that the data from different domains might have different characteristics of noise, which can be utilized in the process of learning the classifier. Therefore, we attempt to enhance the robustness of the classifier by injecting the heterogeneous data artificially into the learning process in order to improve the classification accuracy. Semi-supervised approach was applied for utilizing the heterogeneous data in the process of learning the document classifier. However, the performance of document classifier might be degraded by the unlabeled data. Therefore, we further proposed an algorithm to extract only the documents that contribute to the accuracy improvement of the classifier.

• 한국의류학회지
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• 제46권6호
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• pp.1142-1160
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• 2022

Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.

• 대한원격탐사학회지
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• 제34권4호
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• pp.693-702
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• 2018

With the development of data analysis methods and data processing capabilities, semantic analysis of floorplans has been actively studied. Therefore, studies for extracting text information from drawings have been conducted for semantic analysis. However, existing research that separates rasterized text from floorplan has the problem of loss of text information, because when graphic and text components overlap, text information cannot be extracted. To solve this problem, this study defines the morphological characteristics of the text in the floorplan, and classifies the class of the corresponding region by applying the class of the SIFT key points through the SVM models. The algorithm developed in this study separated text components with a recall of 94.3% in five sample drawings.

• International Journal of Contents
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• 제9권2호
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• pp.8-13
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• 2013

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

• 한국건설관리학회논문집
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• 제17권5호
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• pp.89-96
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• 2016

건설 프로젝트에서 생산되는 대부분의 데이터는 텍스트 기반의 비정형 데이터이다. 계약서, 시방서, RFi 등 수많은 텍스트 문서들을 효과적으로 분석하기 위해서는 텍스트 마이닝과 같은 비정형 텍스트 데이터 분석 방법이 필요하다. 이에 본 연구에서는 과거에 수행되었던 해외건설공사 프로젝트의 입찰 관련 문서들을 대상으로 텍스트 마이닝을 실시하였으며, 그 결과 빈출단어의 유형, 단어들 간의 연관관계, 문서들의 토픽 유형들에 대한 파악이 가능하였다. 본 연구는 텍스트 마이닝을 활용한 해외건설공사 입찰 정보 분석을 통해 비정형 텍스트 데이터를 효과적으로 분석할 수 있는 방안을 제시하였다는 점에서 의의가 있으며, 향후 관련 분야 연구를 확장시킬 수 있는 기반을 마련할 수 있을 것이라 기대한다.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.817-824
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• 2003

We employed a hierarchical document classification method to classify a massive collection of electronic medical records(EMR) written in both Korean and English. Our experimental system has been learned from 5,000 records of EMR text data and predicted a newly given set of EMR text data over 68% correctly. We expect the accuracy rate can be improved greatly provided a dictionary of medical terms or a suitable medical thesaurus. The classification system might play a key role in some clinical decision support systems and various interpretation systems for clinical data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.