• Journal of the Korean Society of Physical Medicine
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• v.14 no.3
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• pp.55-62
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• 2019

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients' concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.495-502
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• 2022

Web crawler, which is mainly used to collect text on the web today, is difficult to maintain and expand because researchers must implement different collection logic by collection channel after analyzing tags and styles of HTML documents. To solve this problem, the web crawler should be able to collect text by formalizing HTML documents to the same structure. In this paper, we designed and implemented WCTT(Web Crawling system based on Tag path and Text appearance frequency), a web crawling system that collects text with a single collection logic by formalizing HTML documents based on tag path and text appearance frequency. Because WCTT collects texts with the same logic for all collection channels, it is easy to maintain and expand the collection channel. In addition, it provides the preprocessing function that removes stopwords and extracts only nouns for keyword network analysis and so on.

• Journal of Information Technology Services
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• v.19 no.3
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• pp.117-137
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• 2020

The development of information technology leads to changes in various industries. In particular, the health care industry is more influenced so that it is focused on. With the widening of the health care market, the market of smart device based personal health care also draws attention. Since a variety of fitness applications for smartphone based exercise were introduced, more interest has been in the health care industry. But although an amount of use of mobile fitness applications increase, it fails to lead to a sustained use. It is necessary to find and understand what matters for mobile fitness application users. Therefore, this study analyze the reviews of mobile fitness application users, to draw key factors, and thereby to propose detailed strategies for promoting mobile fitness applications. We utilize text mining techniques - LDA topic modeling, term frequency analysis, and keyword extraction - to draw and analyze the issues related to mobile fitness applications. In particular, the key factors drawn by text mining techniques are explained through the concept of user experience. This study is academically meaningful in the point that the key factors of mobile fitness applications are drawn by the user experience based text mining techniques, and practically this study proposes detailed strategies for promoting mobile fitness applications in the health care area.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.835-838
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• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.113-124
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• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.83-103
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• 2015

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining-extracting meaningful information from unstructured text data-has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.279-285
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• 2023

This study provides academic implications by considering trends of domestic research regarding therapy for Mental disorder schizophrenia and psychosocial. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 65 papers have been collected The result of this study is as follows. First, collected data were visualized through analysis of keywords by using word cloud method. Second, keywords such as intervention, schizophrenia, research, patients, program, effect, society, mind, ability, function were recorded with highest frequency resulted from keyword frequency analysis. Third, LDA (latent Dirichlet allocation) topic modeling result showed that classified into 3 keywords: patient, subjects, intervention of psychosocial, efficacy of interventions. Fourth, the social network analysis results derived connectivity, closeness centrality, betweennes centrality. In conclusion, this study presents significant results as it provided basic rehabilitation data for schizophrenia and psychosocial therapy through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of schizophrenia and psychosocial therapy through text mining and social network analysis.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.223-231
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• 2022

The application of computer software into the linguistic analysis of texts proves useful to arrive at concise and authentic results from large data texts. Based on this assumption, this paper employs a Computer-Aided Text Analysis (CATA) and a Critical Discourse Analysis (CDA) to explore the manipulative strategies of positive/negative presentation in Orwell's Animal Farm. More specifically, the paper attempts to explore the extent to which CATA software represented by the three variables of Frequency Distribution Analysis (FDA), Content Analysis (CA), and Key Word in Context (KWIC) incorporate with CDA decipher the manipulative purposes beyond positive presentation of selfness and negative presentation of otherness in the selected corpus. The analysis covers some CDA strategies, including justification, false statistics, and competency, for positive self-presentation; and accusation, criticism, and the use of ambiguous words for negative other-presentation. With the application of CATA, some words will be analyzed by showing their frequency distribution analysis as well as their contextual environment in the selected text to expose the extent to which they are employed as strategies of positive/negative presentation in the text under investigation. Findings show that CATA software contributes significantly to the linguistic analysis of large data texts. The paper recommends the use and application of the different CATA software in the stylistic and corpus linguistics studies.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.27-33
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• 2008

In this paper, we propose a method of automatically constructing a dictionary by extracting unknown words from given eojeols in order to improve the performance of a Korean morphological analyzer. The proposed method is composed of a dictionary construction phase based on full text analysis and a dictionary construction phase based on web document frequency. The first phase recognizes unknown words from strings repeatedly occurred in a given full text while the second phase recognizes unknown words based on frequency of retrieving each string, once occurred in the text, from web documents. Experimental results show that the proposed method improves 32.39% recall by utilizing web document frequency compared with a previous method.

• Korean Journal of Childcare and Education
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• v.16 no.6
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• pp.253-272
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• 2020

Objective: This study aimed to analyze the counseling contents of daycare center teachers by using text mining and semantic network analysis methods to find the necessary support directions for daycare teachers and to improve the quality of child-care. Methods: Five hundred thirteen cases of counseling recorded on the open bulletin board of online counseling (Naver Bands for Nursery Teacher Counseling) were collected, and frequency analysis, centrality solidarity analysis, and machine learning-based topic analysis were conducted using the NetMiner4.3 program. Results: First, 'teacher-to-child ratio' was highest in the frequency. Second, 'colleagues' were all high in all centrality analysis. Third, machine learning-based topical analysis shows that the topics were categorized as subjects about 'childcare and education', 'working environment that supports professional development' and 'working condition', and among them, 'first-time teacher concerns' accounted for 44% of the total counseling content. Conclusion/Implications: This study implied that it is necessary to provide high-quality child-care and education to infants by lowering the 'teacher-to-child ratio', and a systematic program is needed to help improve effective communication skills in interpersonal relationships such as between parents, fellow teachers, and principals. In addition, self-development and efforts to improve teachers expertise should be prioritized in order to improve infant care quality and quality of teachers.