• Journal of Nutrition and Health
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• v.56 no.3
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• pp.330-347
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• 2023

Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

• Smart Media Journal
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• v.12 no.6
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• pp.27-34
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• 2023

As Netflix usage has increased due to the COVID-19 pandemic, users' experiences with the service have also increased. Therefore, this study aims to conduct topic modeling analysis based on Netflix review data to explore the changes in Netflix user experience and service before and after the COVID-19 pandemic. We collected Netflix app review data from the Google Play Store using the Google Play Scraper library, and used topic modeling to examine keyword differences between app reviews before and after the pandemic. The analysis revealed four main topics: Netflix app features, Netflix content, Netflix service usage, and Netflix overall reviews. After the pandemic, when user experience increased, users tended to use more diverse and detailed keywords in their reviews. By using Netflix review data to analyze users' opinions, this study shows the changes in user experience of Netflix services before and after the pandemic, which can be used as a guide to strengthen competitiveness in the competitive OTT market.

• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.1-9
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• 2018

The proportion of users and companies using open source continues to grow. The size of open source software market is growing rapidly not only in foreign countries but also in Korea. However, compared to the continuous development of open source software, there is little research on open source software subject classification, and the classification system of software is not specified either. At present, the user uses a method of directly inputting or tagging the subject, and there is a misclassification and hassle as a result. Research on open source software classification can also be used as a basis for open source software evaluation, recommendation, and filtering. Therefore, in this study, we propose a method to classify open source software by using machine learning model and propose performance comparison by machine learning model.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.281-290
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• 2017

As various stakeholders are involved in construction projects, disputes between the parties are more likely to occur, which is a very important issue for the participants in the projects. Claims in construction projects, however, are very complex and thus difficult to manage. In particular, as the cause of a claim in the preceding stage that has not been resolved in a timely manner has an effect on the cause of a claim in the following stage, it is difficult to find a point of compromise regarding a claim caused by the relationship between the causes that occur in the preceding and following stages. In this regard, this study sought to examine the rules for the generation of change order claims, which occur most frequently among the construction claims, and thus to select the key management targets through the analysis of the relationship between the causes of claims arising in the preceding and following stages for the efficient management of claims. It is expected that the use of rules for the generation of change order claims as well as of representative and similar cases will help the construction practitioners in judging claims, considering the relationships among the causes of the claims. Meanwhile, in this study, association analysis was conducted regarding the causes of the occurrence of change order claims in a design-build delivery method, and therefore, it is necessary to verify the effectiveness of the method when applied to other delivery methods.

• The Korean Fashion and Textile Research Journal
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• v.25 no.6
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• pp.715-724
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• 2023

This study analyzed the shifts that occurred before and after the outbreak of COVID-19 by scrutinizing digital keywords derived from prominent culture media, such as books and instagram. The analysis identified trends rooted in digital terminology. For this study, the period 2017 to 2022 was divided into three-year segments, before and after the outbreak of COVID-19. Subsequently, an analysis was conducted using digital keywords to assess the number of digital-related books and book hashtags, the number of instagram mentions, and relevant keywords. We found that COVID-19 exerted a discernible influence on information related to digital keywords, substantially impacting both the book publishing market and instagram. Notably, digital-related books have been published in a variety of fields since the outbreak, and new fields are emerging. The year 2020 saw the most significant growth in the mentions of digital terms on instagram. Such terms were used in conjunction with terminology related to people working in a digital environment, endeavors aimed at revenue generation in online spaces, leisure activities associated with art and culture, and online service platforms. Through the analysis of digital keywords, this study is expected to contribute to the understanding of digital trends and their future trajectories.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.292-300
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• 2023

This paper proposes a patent discovery method and strategy for Generative AI-related patents by utilizing qualitative evaluation indicators established based on the core components of the technology. Currently, the evaluation of patent quality relies on quantitative indicators, but existing quantitative indicators cannot represent the characteristics of Generative AI technology, making it difficult to accurately evaluate. Therefore, there is a need for additional qualitative indicators that consider technical characteristics based on patent claims, which can reveal the actual strength of the patent. In this paper, we propose a new evaluation index considering the technical characteristics of Generative AI. Core patents were selected using the proposed evaluation index, and the appropriateness of the proposed index was verified through the existing quantitative evaluation method for the selected core patents.

• Korea Trade Review
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• v.48 no.1
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• pp.55-81
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• 2023

The purpose of this study is to analyze the main issues and trends of Korean trade, and to draw implications for future research regarding trade rules. A total of 476 academic journal are analyzed using English keyword searched for 'Trade Rules' from 2000 to July 2022 in the Korean Journal Citation Index data base. The analysis methodology includes co-occurrence network and topic trend analysis which is a kind of text mining methods. The results shows that key words representing Korea's trade trend fall into four categories in which the number of research journals has rapidly increased, which are Topic 4 (Investment Treaty), Topic 7 (Trade Security), Topic 8 (China's Protectionism), and Topic 11 (Trade Settlement). The major background for these topics is the tension between the United States and China threatening the existing international trade system. A detailed study for China's protectionism, changes in trade security system, and new investment agreements, and changes in payment methods will be the challenges in near future.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.57-64
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• 2023

This study analyzed the perceptions of elementary and secondary school teachers regarding the importance of SW/AI education in fostering students' fundamental knowledge and the necessity of integrating SW/AI into education. A total of 830 elementary and secondary school teachers were selected as study subjects using the judgment sampling method. The analysis of survey data revealed that elementary and secondary teachers exhibited a strong awareness of the importance and necessity of SW/AI education, irrespective of school characteristics, region, educational experience, or prior involvement in SW and AI education. Nevertheless, the primary reasons for not implementing SW/AI education were identified as excessive workload and a lack of pedagogical expertise. An analysis of opinions on the essential conditions for implementing SW/AI education revealed that workload reduction, budget support, teacher training to enhance teacher competency, content distribution, expansion of subject-linked courses, and dedicated instructional time allocation were the major influencing factors. These findings indicate a significant demand for comprehensive instructional support and teacher capacity-building programs.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.65-73
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• 2024

This paper aims to identify the major research trends in organizational effectiveness over the past 20 years. For this purpose, SCOPUS, an international academic database provided by Elsevier, was used to identify research trends in organizational effectiveness over the past 24 years (2000~2023). According to the frequency analysis, there were 2,789 cases of organizational, 2,714 cases of effectiveness, 850 cases of management, 689 cases of performance, 632 cases of organizations, and 597 cases of leadership. Trend analysis. While effectiveness and organizational have been consistently researched, the trends of leadership and management have been declining in recent years. LDA analysis shows that effectiveness and organizational are important topics. This shows that it is important to be able to predict the future when it is difficult to predict the future. The results of this study can be used as a guide for companies to establish organizational management at a strategic level and improve organizational effectiveness.