• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.43-51
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• 2022

The purpose of this study is to provide basic information necessary for the establishment of mobile service marketing strategies, educational service development, and engineering education for Generation Z by analyzing the utilitization of various applications by Gen Z. To this end, 177 essays on mobile service usage experience were collected, major topics were analyzed using topic modeling, and these were visualized through word cloud analysis. As a result of the study, the main topics were related to 'transportation' such as movement and public transportation, 'personal management' such as schedule management, financial management, food management, 'transaction' such as checkout, meeting, purchase, 'leisure' such as eating out, travel, study, culture. Additionally, words such as time, thought, people, life, bus, information, confirmation, payment, KakaoTalk, and so on were found to have a high of frequency of use. Also, there was found to be a difference between topics by college. This study is meaningful in that it collected essays, which are unstructured data, and analyzed them through topic modeling.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.285-293
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• 2021

With the advent of the 4th Industrial Revolution, AI utilization capabilities are being emphasized in various industries, but AI education design and curriculum research as universal education is currently lacking. This study offers a design for universal AI education to further cultivate its use in universities. For the AI basic education design, a questionnaire was conducted for experts three times, and the reliability of the derived design contents was verified by reflecting the results. As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived were data structure understanding and processing, visualization, word cloud, public data utilization, and machine learning concept understanding and utilization. The educational design content derived through this study is expected to increase the value of competency-centered AI universal education in the future.

• Journal of People, Plants, and Environment
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• v.23 no.2
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• pp.233-243
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• 2020

This study collects social big data used in various fields in the past 9 years and explains the patterns of major keywords of the arboretums and botanical gardens to use as the basic data to establish operational strategies for future arboretums and botanical gardens. A total of 6,245,278 cases of data were collected: 4,250,583 from blogs (68.1%), 1,843,677 from online cafes (29.5%), and 151,018 from knowledge search engine (2.4%). As a result of refining valid data, 1,223,162 cases were selected for analysis. We came up with keywords through big data, and used big data program Textom to derive keywords of arboretums and botanical gardens using text mining analysis. As a result, we identified keywords such as 'travel', 'picnic', 'children', 'festival', 'experience', 'Garden of Morning Calm', 'program', 'recreation forest', 'healing', and 'museum'. As a result of keyword analysis, we found that keywords such as 'healing', 'tree', 'experience', 'garden', and 'Garden of Morning Calm' received high public interest. We conducted word cloud analysis by extracting keywords with high frequency in total 6,245,278 titles on social media. The results showed that arboretums and botanical gardens were perceived as spaces for relaxation and leisure such as 'travel', 'picnic' and 'recreation', and that people had high interest in educational aspects with keywords such as 'experience' and 'field trip'. The demand for rest and leisure space, education, and things to see and enjoy in arboretums and botanical gardens increased than in the past. Therefore, there must be differentiation and specialization strategies such as plant collection strategies, exhibition planning and programs in establishing future operation strategies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.423-429
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• 2022

This study is a study on the perceptions of pre-primary early childhood teachers on teaching and learning methods for infants and toddlers in the early childhood teacher training process. This is a mixed study using word cloud analysis and qualitative case analysis on the subject, focusing on literature research and understanding of pre-primary early childhood teachers' 'teaching and learning methods for infants and toddlers'. The purpose of this study was to find out the meaning of a early childhood teacher through thoughts on teaching and learning methods for infants, difficulties, points to be learned, teaching competency to be good as a teacher, and experiences for teaching professionalism. Through the results of this study, it is expected to find a way to increase their sense of efficacy on teaching and learning methods when conducting classes for young children in the future, and to provide basic data for improving the quality of early childhood education.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.75-83
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• 2022

Korea, which has the highest pedestrian fatality rate among OECD countries, is making efforts to improve the safe walking environment by enacting laws focusing on pedestrian. Spatial clustering was conducted with scan statistics after examining the social network data related to traffic accidents for children and seniors. The word cloud was used to examine people's recognition Campaigns for children and literature survey for seniors were in main concern. Naedang and Yongsan are the regions with the highest relative risk of weak pedestrian for children and seniors. On the contrary, Bongmu and Beomeo are the lowest relative risk region. Naedang-dong and Yongsan-dong of Daegu Metropolitan City were identified as vulnerable areas for pedestrian safety due to the high risk of pedestrian accidents for children and the elderly. This means that the scan statistics are effective in searching for traffic accident risk areas.

• Journal of Korean society of Dental Hygiene
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• v.23 no.5
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• pp.351-360
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• 2023

Objectives: This study is based on a visiting oral health care intervention program in the community care. This qualitative study was conducted through in-depth interviews to identify awareness and attitudes regarding intervention program among older adults. Methods: The research team visited the homes of the target older adults and conducted in-depth interviews for approximately an hour using a semi-structured questionnaire. The collected voice recordings were transcribed using Clova Note, and AI program by Naver. Using the 'Word Cloud Generator 3.7' program, words of high importance and interest from interview answers were extracted, visualized, and analyzed. Results: Participating older adults acknowledged that their quality of life related to oral health could be improved by increasing the level of oral health awareness and oral health knowledge through the intervention program. In addition, the older adults indicated that their oral hygiene management ability improved compared to before the intervention through expert oral hygiene management and oral health education. Further, as the level of oral health knowledge increased, so too did satisfaction with the intervention program increase. Conclusions: The intervention program for visiting oral health care showed a positive effect on the awareness and attitude of older adults. Thus, it is suggested that education for continuous competency enhancement of dental hygienists and multidisciplinary education for the improvement of general health and quality of life of older adults should be promoted.

• Industry Promotion Research
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• v.8 no.4
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• pp.131-137
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• 2023

This study collected and analyzed articles related to military sex crimes covered in the news from February 2019 to May 28, 2022 in order to identify problems arising from sexual crimes in the military. In order to understand the current status of military sexual violence reported in the media, articles were collected using BIGKinds, a news big data analysis system, and using the Textom program, the study was conducted using frequency analysis by period, word cloud, and semantic network analysis techniques for keywords. The study was conducted using the technique. As a result of data analysis, first, it was confirmed that the public's attention was focused on the victims in reports related to sex crimes within the military. Second, the problem of the lukewarm system of the relevant authorities in responding to sex crimes was revealed. Third, there was a lack of support for victims of sex crimes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.370-396
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• 2024

COVID-19 was declared a pandemic by the World Health Organization (WHO) on 30 January 2020. The lifestyle of people all over the world has changed since. In most cases, the pandemic has appeared to create severe mental disorders, anxieties, and depression among people. Mostly, the researchers have been conducting surveys to identify the impacts of the pandemic on the mental health of people. Despite the better quality, tailored, and more specific data that can be generated by surveys,social media offers great insights into revealing the impact of the pandemic on mental health. Since people feel connected on social media, thus, this study aims to get the people's sentiments about the pandemic related to mental issues. Word Cloud was used to visualize and identify the most frequent keywords related to COVID-19 and mental health disorders. This study employs Majority Voting Ensemble (MVE) classification and individual classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) to classify the sentiment through tweets. The tweets were classified into either positive, neutral, or negative using the Valence Aware Dictionary or sEntiment Reasoner (VADER). Confusion matrix and classification reports bestow the precision, recall, and F1-score in identifying the best algorithm for classifying the sentiments.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.99-107
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• 2024

In an environment where computer-related technologies are rapidly changing, cyber threats continue to emerge as they are advanced and diversified along with new technologies. Therefore, in this study, we would like to collect security-related news articles, conduct LDA topic modeling, and examine trends. To that end, news articles from January 2020 to August 2023 were collected and major topics were derived through LDA analysis. After that, the flow by topic was grasped and the main origin was analyzed. The analysis results show that ransomware attacks in 2021 and hacking of virtual asset exchanges in 2023 are major issues in the recent security sector. This allows you to check trends in security issues and see what research should be focused on in the future. It is also expected to be able to recognize the latest threats and support appropriate response strategies, contributing to the development of effective security measures.