• Journal of Digital Convergence
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• v.19 no.2
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• pp.33-38
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• 2021

The convergence trend is the core of the 4th industrial revolution, and due to such expectations and possibilities, various countermeasures are being sought in diverse fields. This study conducted a quantitative analysis to identify the trend of convergence research over the past 10 years. Specifically, major research keywords were extracted, word cloud techniques were applied, and visualized to identify trends in academic research on convergence. To this end, research papers from 2012 to 2020 published in journal of digital convergence were investigated. The analysis period was divided into two periods: the former 4 years(2012-2015) and the latter 4 years(2016-2019) to confirm the difference in research trends. In addition, the research papers of 2020 were analyzed in order to more clearly understand the changes in the research trend of the last year due to the COVID-19. The results of this study are significant in that they can be used as useful basic data for future research and to understand research trends as keywords in the field of convergence.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.267-278
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• 2021

As time goes by, the amount of data is increasing regardless of government, business, domestic or overseas. Accordingly, research on big data is increasing in academia. Statistics is one of the major disciplines of big data research, and it will be interesting to understand the research trend of statistics through big data in the growing number of papers in statistics. In this study, we analyzed what studies are being conducted through abstract data of statistical papers in Korea and abroad. Research trends in domestic and international were analyzed through the frequency of keyword data of the papers, and the relationship between the keywords was visualized through the Word Embedding method. In addition to the keywords selected by the authors, words that are importantly used in statistical papers selected through Textrank were also visualized. Lastly, 10 topics were investigated by applying the LDA technique to the abstract data. Through the analysis of each topic, we investigated which research topics are frequently studied and which words are used importantly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• Smart Media Journal
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• v.9 no.4
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• pp.44-51
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• 2020

Since the mid-to-late 1990s, there have obviously been many changes in the North Korean economy. Since the change has been more pronounced since Kim Jong Un took power in 2012, the purpose of the paper is to track the trend of economic policy by timing. In this paper, I use LDA Topic Modeling, a text-mining analyzer method, to analyze the economics journal "Economic Research," which is a representative literature in the economic field published in North Korea. An in-depth analysis of the "economic research," which has an unrivaled position as an economic journal produced in North Korea, can be said to be an essential task in tracking the reality, limitations facing the economy and alternatives that North Korean authorities are aware of. Through the "Economic Research," where various topics of debate on the North Korean economy are hidden, the North Korean leader's economic policy flow is examined and the contents of the "change" intended by the current Kim Jong-un regime are analyzed.

• The Journal of Information Systems
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• v.30 no.4
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• pp.277-301
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• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• Journal of Creative Information Culture
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• v.6 no.2
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• pp.57-64
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• 2020

As the information society gradually advances, various opinions overflow and their complexity increases. As the results, it was made more difficult to derive important issues and properly respond to those problems. Accordingly, it is necessary to get a handle on emerging problems in education in addition to existing discourses and issues. This study aimed at examining the issues of education by analyzing the petitions posted under 'parenting and education' category on National Petition board. In order to offer objective and detailed results, we employed the topic modeling based LDA algorithm, which is an effective method to extract topics in multiple documents. Nine topics were derived as the result of the analysis and the relationship among those topics was visualized. The values of this study exist in that the derived topics represent important issues that reflect the public opinions.

• Fashion & Textile Research Journal
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• v.24 no.5
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• pp.593-604
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• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.

• Land and Housing Review
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• v.13 no.2
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• pp.81-90
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• 2022

This study is based on social needs for green healing spaces assumed to enhance mental health in a city. This study proposes development directions through the analysis of modern social recognition factors for neighborhood gardens. As a research method, web information data was collected using Textom among big data tools. Text Mining was conducted to extract elements and analyze their relationship through keyword analysis, network analysis, and cluster analysis. As a result, first, the healing space and the healing environment were creating an eco-friendly healthy environment in a space close to the neighborhood within the city. Second, neighborhood gardens included projects and activities that involved government, local administration, and citizens by linking facilities as well as living culture and urban environments. These gardens have been reinforced through green welfare and service programs. In conclusion, friendly gardens in the neighborhood for the purpose of public interest, which are beneficial to mental health, are green infrastructures as a healing environment that can produce positive effects.

• The Journal of Korean Medicine
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• v.42 no.4
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• pp.120-132
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• 2021

Objectives: The prevalence of Parkinson's disease is on an upward trend along with an increase in the aging population but there is no available treatment that halts the progression of neurodegeneration. This study reports a numerical analysis on Donguibogam and suggests novel herbal drugs, which have never been researched before but found to be deemed effective in this study. Methods: Referring to 71 Korean medicine symptom terms that represent the symptoms of Parkinson's disease, 4170 prescriptions described in Donguibogam were classified into two groups based on whether their main effects were effective for Parkinson's disease or not. Comparing the two groups, the chi-square test was performed to select statistically significant herbs, while the t-test, Wilcoxon test, and descriptive statistics were performed to determine the appropriate dose. Results: One hundred and twenty-seven prescriptions effective for Parkinson's disease were identified. The chi-square test determined 17 herbs that are effective for symptomatic treatment. Among the medicinal herbs, the authors suggest Osterici seu Notopterygii Radix et Rhizoma, Ephedrae Herba, Aconiti Tuber, Myrrha, Sinomeni Caulis et Rhizoma, and Aconiti Kusnezoffii Tuber as herbal candidates that have never been studied for Parkinson's disease. Through the statistical tests, it was judged that the mean value of the dose of the entire prescription was the appropriate dose for each herb. Conclusions: Seventeen herbs were selected for Parkinson's disease and the appropriate daily dose were calculated. Furthermore, this study presented a new process that applies a statistical method to traditional medical literature and preselecting herbs deemed effective for specific diseases.

• 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.