• 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.

• Korea Trade Review
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• v.47 no.4
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• pp.137-159
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• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.596-603
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• 2015

A concept network is an essential knowledge base for semantic search engines, personalized search systems, recommendation systems, and text mining. Recently, studies of extending concept representation using external ontology have been frequently conducted. We thus propose a new way of building 3-dimensional text model-based concept networks using the world knowledge-level Wikipedia ontology. In fact, it is desirable that 'concepts' derived from text documents are defined according to the theoretical framework of formal concept analysis, since relationships among concepts generally change over time. In this paper, concept networks hidden in a given document collection are extracted more reasonably by representing a concept as a term-by-document matrix.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.40-56
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• 2022

This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package "rvest." The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. "LIS Recruitment," "LIS Issues," "Other Discussion," "LIS Education," "LIS Research," "LIS Exams," "General Information related to Library," "LIS Admission," "Library and Professional Activities," and "Information Communication Technology (ICT)." It was found that the majority of the posts belonged to "LIS Exam," followed by "Other Discussions" and "General Information related to the Library."

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.359-367
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• 2020

The purpose of this study is to explore text data of nursing start-up. 55 literatures were extracted from MEDLINE, Embase and Cochrane Library Data BASE. Text network analysis applied by using python network program. Key words with highest frequency and degree centrality were 'business', 'care', 'nursing', 'healthcare', 'service'. Keywords with highest degree centrality were 'mission', 'vision', 'team'. Based on the results nursing entrepreneurship support should be provided to develop competitive nursing services reflecting the specificity and science of nursing, to strengthen business competencies essential for nursing entrepreneurship, to expand nursing expertise and to present role models. The result will serve a basement to development systematic educational program and theory in nursing start-up.

• CELLMED
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• v.5 no.2
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• pp.11.1-11.5
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• 2015

Traditional East Asian Medicine (TEAM) prescriptions typically consist of several herbs based on the assumption that the herbs operate synergistically and/or cooperate on several related pathways simultaneously. This is a general concept that is widely accepted in TEAM, but it has not been tested systematically. To check this assumption statistically, we have text mined traditional Korean medicine text the Inje-ji(仁濟志, Collections of benevolent savings), a text that contains more than 5000 herb-cocktail prescriptions. We created herb-pairing network based on herb-herb pairing specificity and performed a systematic network analysis. Herbs were shown to be used selectively with other herbs and not randomly. Moreover, herb pairs were more specifically associated with symptoms than were single herbs. Single herbs and combinations of herbs specifically used for diabetes mellitus were successfully identified. As conclusion, herb-pairings in TEAM are not randomly constructed; instead, each herb was selectively used with other herbs. In terms of statistical significance, herb pairs were more specifically associated with symptoms than were single herbs alone. Collectively, these results suggest that it may be important to understand the interactions among multiple ingredients contained in herb pairs rather than trying to identify a single compound to resolve symptoms.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.189-197
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• 2022

Today, we are exposed to various text-based media such as newspapers, Internet articles, and SNS, and the amount of text data we encounter has increased exponentially due to the recent availability of Internet access using mobile devices such as smartphones. Collecting useful information from a lot of text information is called text analysis, and in order to extract information, it is performed using technologies such as Natural Language Processing (NLP) for processing natural language with the recent development of artificial intelligence. For this purpose, a morpheme analyzer based on everyday language has been disclosed and is being used. Pre-learning language models, which can acquire natural language knowledge through unsupervised learning based on large numbers of corpus, are a very common factor in natural language processing recently, but conventional morpheme analysts are limited in their use in specialized fields. In this paper, as a preliminary work to develop a natural language analysis language model specialized in the railway field, the procedure for construction a corpus specialized in the railway field is presented.

• Journal of The Korean Association For Science Education
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• v.40 no.1
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• pp.41-50
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• 2020

Looking at the understanding of scientific concepts from a linguistic perspective, it is very important for students to develop a deep and sophisticated understanding of words used in scientific concept as well as the ability to use them correctly. This study intends to provide the basis for productive knowledge education of scientific words by noting that the foundation of productive knowledge teaching on scientific words is not well established, and by exploring ways to teach the relationship among words that constitute scientific concept in a productive and effective manner. To this end, we extracted the relationship among the words that make up the scientific concept from the text of science textbook by using quantitative text analysis methods, second, qualitatively examined the meaning of the word relationship extracted as a result of each method, and third, we proposed a writing activity method to help improve the productive knowledge of scientific concept words. We analyzed the text of the "Force and motion" unit on first grade science textbook by using four methods of quantitative linguistic analysis: word cluster, co-occurrence, text network analysis, and word-embedding. As results, this study suggests four writing activities, completing sentence activity by using the result of word cluster analysis, filling the blanks activity by using the result of co-occurrence analysis, material-oriented writing activities by using the result of text network analysis, and finally we made a list of important words by using the result of word embedding.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.1-31
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• 2023

This study conducted user experience evaluation by introducing various text mining techniques along with topic modeling techniques for mobile menstrual cycle measurement applications that are closely related to women's health and analyzed the results by combining them with a honeycomb model. To evaluate the user experience revealed in the menstrual cycle measurement application review, 47,117 Korean reviews of the menstrual cycle measurement application were collected. Topic modeling analysis was conducted to confirm the overall discourse on the user experience revealed in the review, and text network analysis was conducted to confirm the specific experience of each topic. In addition, sentimental analysis was conducted to understand the emotional experience of users. Based on this, the development strategy of the menstrual cycle measurement application was presented in terms of accuracy, design, monitoring, data management, and user management. As a result of the study, it was confirmed that the accuracy and monitoring function of the menstrual cycle measurement of the application should be improved, and it was observed that various design attempts were required. In addition, the necessity of supplementing personal information and the user's biometric data management method was also confirmed. By exploring the user experience (UX) of the menstrual cycle measurement application in-depth, this study revealed various factors experienced by users and suggested practical improvements to provide a better experience. It is also significant in that it presents a methodology by combines topic modeling and text network analysis techniques so that researchers can closely grasp vast amounts of review data in the process of evaluating user experiences.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.89-102
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• 2024

This study aims to examine the characteristics of the user experience of New Town neighborhood parks and explore issues that diversify the experience of the parks. In order to quantitatively analyze a large amount of park visitors' experiences, text-based Naver blog reviews were collected and analyzed. Among the Phase 1 and 2 New Towns, the parks with the highest user experience postings were selected for each city as the target of analysis. Blog text data was collected from May 20, 2003, to May 31, 2022, and analysis was conducted targeting Ilsan Lake Park, Bundang Yuldong Park, Gwanggyo Lake Park, and Dongtan Lake Park. The findings revealed that all four parks were used for everyday relaxation and recreation. Second, the analysis underscores park's diverse user groups. Third, the programs for parks nearby were also related to park usage. Fourth, the words within the top 20 rankings represented distinctive park elements or content/programs specific to each park. Lastly, the results of the network analysis delineated four overarching types of park users and the networks of four park user types appeared differently depending on the park. This study provides two implications. First, in addition to the naturalistic characteristics, the differentiation of each park's unique facilities and programs greatly improves public awareness and enriches the individual park experience. Second, if analysis of the context surrounding the park based on spatial information is performed in addition to text analysis, the accuracy of interpretation of text data analysis results could be improved. The results of this study can be used in the planning and designing of parks and greenspaces in the Phase 3 New Towns currently in progress.