• The Journal of Korean Medical History
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• v.28 no.1
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• pp.135-141
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• 2015

The purpose of this study is to analyze vocabulary found in "Donguibogam", one of the medical books in mid-Chosun, through Corpus-based analysis, one of the text analysis methods. According to it, Donguibogam has total 871,000 words in it, and Chinese characters used in it are total 5,130. Among them, 2,430 characters form 99% of the entire text. The most frequently appearing 20 Chinese characters are mainly function words, and with this, we can see that "Donguibogam" is a book equipped with complete forms of sentences just like other books. Examining the chapters of "Donguibogam" by comparison, Remedies and Acupuncture indicated lower frequencies of function words than Internal Medicine, External Medicine, and Miscellaneous Diseases. "Yixuerumen (Introduction to Medicine)" which influenced "Donguibogam" very much has lower frequencies of function words than "Donguibogam" in its most frequently appearing words. This may be because "Yixuerumen" maintains the form of Chileonjeolgu (a quatrain with seven Chinese characters in each line with seven-word lines) and adds footnotes below it. Corpus-based analysis helps us to see the words mainly used by measuring their frequencies in the book of medicine. Therefore, this researcher suggests that the results of this analysis can be used for education of Chinese characters at the college of Korean Medicine.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.380-389
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• 2021

This study aimed to reveal the perceptions and trends of digital fashion technology through an informational approach. A big data analysis was conducted after collecting the text shown in a web environment from April 2019 to April 2021. Key words were derived through text mining analysis and network analysis, and the structure of perception of digital fashion technology was identified. Using textoms, we collected 8144 texts after data refinement, conducted a frequency of emergence and central component analysis, and visualized the results with word cloud and N-gram. The frequency of appearance also generated matrices with the top 70 words, and a structural equivalent analysis was performed. The results were presented with network visualizations and dendrograms. Fashion, digital, and technology were the most frequently mentioned topics, and the frequencies of platform, digital transformation, and start-ups were also high. Through clustering, four clusters of marketing were formed using fashion, digital technology, startups, and augmented reality/virtual reality technology. Future research on startups and smart factories with technologies based on stable platforms is needed. The results of this study contribute to increasing the fashion industry's knowledge on digital fashion technology and can be used as a foundational study for the development of research on related topics.

• Journal of Korean Library and Information Science Society
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• v.47 no.1
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• pp.339-360
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• 2016

This study is intended to analyze the actual condition of operating the full-text information disclosure system of government and Officials' Perceptions. According to the results of analysis, the public servant group's level of full-text information disclosure system was high. With regard to the positive and negative function of full-text information disclosure, an expectation for positive function was high in the aspect of assuring the transparency of administration. And public servants were worried about an increase in the burden of administrative duties. With regard to factors in hindering the development of full-text information disclosure, it was shown that the percentage of clients' abuse and misuse of full-text information was the highest. For the activation of full-text information disclosure system, it is necessary to prepare measures for preventing the abuse and misuse of full-text information.

• The Journal of Information Systems
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• v.28 no.2
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• pp.153-179
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• 2019

Purpose This study collects the issues about work-life balance in Korea and United States and suggests the specific plans for work-life balance by the comparison and analysis. The objective of this study is to contribute to the improvement of people's life quality by understanding the concept of work-life balance that has become the issue recently and offering the detailed plans to be considered in respect of individual, corporate and governmental level for society of work-life balance. Design/methodology/approach This study collects work-life balance related issues through recruit sites in Korea and United States, compares and analyzes the collected data from the results of three text mining techniques such as LDA topic modeling, term frequency analysis and keyword extraction analysis. Findings According to the text mining results, this study shows that it is important to build corporate culture that support work-life balance in free organizational atmosphere especially in Korea. It also appears that there are the differences against whether work-life balance can be achieved and recognition and satisfaction about work-life balance along type of company or sort of working. In case of United States, it shows that it is important for them to work more efficiently by raising teamwork level among team members who work together as well as the role of the leaders who lead the teams in the organization. It is also significant for the company to provide their employees with the opportunity of education and training that enables them to improve their individual capability or skill. Furthermore, it suggests the roles of individuals, company and government and specific plans based on the analysis of text mining results in both countries.

• East Asian mathematical journal
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• v.35 no.4
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• pp.489-508
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• 2019

In this paper we used text mining method to analyze journals of mathematics education posterior to the year of 2016. To figure out trends of mathematics education research. we analyzed the key words largely mentioned in the recent mathematics education journals by Term Frequency and Term Frequency-Inverse Document Frequency method. We also looked at how these keywords match up with the key words that appear of education to prepare for future society. This result can infer the characteristics of mathematics education research in the aspect upcoming research topics.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.355-360
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• 2014

Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts' evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

• Journal of Platform Technology
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• v.8 no.1
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• pp.33-40
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• 2020

The purpose of this study is to identify factors in selecting the elective ICT utilization lecture and to find positive and negative elements of the lecture through conducting topic modeling analysis of text mining of the narrative lecture evaluation. In order to do so, from pre-processing of data, keyword frequency analysis to wordcloud visualization and topic modeling analysis have been conducted from 'reasons of selecting the lecture,' 'improvements to be made on the lecture,' and 'what I liked about the lecture' categories regarding the ICT utilization lecture which was opened in the second semester of 2019 at M University. The analysis results show that students mostly registered for the ICT utilization lecture at M University to obtain a certificate and the fact being certified and taking the lecture can be done simultaneously is a positive element of taking the lecture. On the other hand, negative element included inconvenience of the classroom setting environment.

• Journal of the Korean Society of Safety
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• v.37 no.3
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• pp.9-15
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• 2022

The analysis of the causes of accidents in workplaces where machines and tools are used is essential to improve the effectiveness and efficiency of safety prevention policies in places of employment in Korea. The causes of workplace accidents are not fully understood mainly due to difficulties in analyzing available descriptive information. This study focuses on the automated accident cause analysis in workplaces based on the accident abstracts found in industrial accident reports written in an unstructured descriptive format. The method proposed in this paper is based on text data mining and uses the keyword search function of Excel software to automate the analysis. The analysis results indicate that the primary reason for the frequency of accidents is related to technical aspects at a stage in which dangerous situations occur in the workplace. Accidents due to managerial causes are typically observed when danger exists in the workplace; however, managerial actions play a more important role in reducing accident severity. A small company tends to use unsafe machines and devices, leading to further accidents due to technical causes, whereas managerial causes are more conspicuous as the company grows. To preclude the occurrence of accidents due to inadequate knowledge, the implementation of safety management and the provision of safety education to elderly workers at the early stage of their employment are particularly important for small companies with less than 100 workers.

• Women's Health Nursing
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• v.29 no.2
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.spc
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• pp.1-8
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• 2023

To investigate the national science and technology policy direction in the field of construction machinery, an analysis was conducted on projects selected as national research and development (R&D) initiatives by the government. Assuming that the project titles contain key keywords, text mining was employed to substantiate this assumption. Project information data spanning nine years from 2014 to 2022 was collected through the National Science & Technology Information Service (NTIS). To observe changes over time, the years were divided into three-year sections. To analyze research trends efficiently, keywords were categorized into groups: 'equipment,' 'smart,' and 'eco-friendly.' Based on the collected data, keyword frequency analysis, N-gram analysis, and topic modeling were performed. The research findings indicate that domestic government R&D in the construction machinery field primarily focuses on smart-related research and development. Specifically, investments in monitoring systems and autonomous operation technologies are increasing. This study holds significance in analyzing objective research trends through the utilization of big data analysis techniques and is expected to contribute to future research and development planning, strategic formulation, and project management.