• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.46-53
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• 2020

The purpose of this study was to examine the direction of social welfare education in the 4th Industrial Revolution as well as discuss the overall direction of social welfare education such as competency and curriculum and the educational dimension of social welfare professionals. Using Text Network Analysis, 223 studies published from 2005 to 2019 in the Korean Journal of Social Welfare Education were examined in order to explore the direction of social welfare education in the 4th Industrial Revolution. Using Word cloud, overall frequency was analyzed. As a result of key words analysis, social welfare education (43), research method (28), and social welfare field practice (23) were analyzed as influential key words. The directions of social welfare education in the 4th Industrial Revolution era are as follows. First, competency, curriculum, and qualifications are necessary in general social welfare education. Second, education centering on social workers and social welfare students, who are social welfare professionals, is necessary. Third, the ethical sensitivity of future social welfare should be carefully established. Finally, the need for a shared welfare system must be further studied.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.504-517
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• 2018

The purpose of this study is to analyze the trends and contents of the research related to young children safety published in the domestic KCI and the candidate journals from 2010 to 2017. To analyze this, we selected 75 articles related to safety for young children published in the KCI and candidate journals from 2010 to 2017. A total of 75 papers were analyzed for frequency, percentage and ${\chi}^2$ using the SPSS Win 23.0 program. The main results of this study are as follows: First, the articles related to young children safety were published the most in 2016 and 2017 and related to infant safety were the least. Next, more than half of the research methods were conducted by quantitative research methods. The results of this study are meaningful in that it presents the necessity of safety education by analyzing trends and contents of research related to young children safety in situations where safety accidents for young children are frequent and the importance of young children safety is more emphasized. It is expected that this research will provide basic data on research topics such as disaster safety who need further research.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.607-619
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• 2019

As software education was introduced through the 2015 revised curriculum, various research activities have been carried out to improve the computational thinking of learners beyond the existing ICT literacy and software utilization education. With this change, the purpose of this study is to examine the research trends of various research activities related to computational thinking which is emphasized in software education. To this end, we extracted the key words from 190 papers related to computational thinking subject published from January 2014 to September 2019, and conducted frequency analysis, word cloud, connection centrality, and topic modeling analysis on the words. As a result of the topical modeling analysis, we found that the main studies so far have included studies on 'computational thinking education program', 'computational thinking education for pre-service teacher education', 'robot utilization education for computational thinking', 'assessment of computational thinking', and 'computational thinking connected education'. Through this research method, it was possible to grasp the research trend related to computational thinking that has been conducted mainly up to now, and it is possible to know which part of computational thinking education is more important to researchers.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.163-171
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• 2022

This study was conducted to understand the research trends and central concepts of middle-aged women' health in Korea. For the analysis of this study, target papers published from 2012 to 2021 were collected by entering the keywords of 'middle-aged woman' or 'menopausal woman'. 1,116 papers were used for analysis. The co-occurrence network of key words was developed and analyzed, and the research trends were analyzed through topic modeling of the LSD by dividing it into five-year units (2012-2016, 2017-2021), and visualized word cloud and sociogram were used. The keywords that appeared the most during the last 10 years were obesity, depression, body composition, stress, and menopause symptom. Five topics analyzed in the thesis data for 5 years from 2012 to 2016 were 'postmenopausal self-efficacy and satisfaction enhancement strategy', 'exercise to manage obesity and risk factors', 'intervention for obesity and stress', 'promotion of happiness and life management' and 'menopausal depression and quality of life' were confirmed. Five topics of research conducted for the next five years (2017-2021) were 'menopausal depression and quality of life', 'management of obesity and cardiovascular risk factors', 'life experience as a middle-aged woman', and 'life satisfaction and psychological well-being' and 'menopausal symptom relief strategy'. Through the results, the trend of research topics related to middle-aged women's health over the past 10 years have been identified, and research on health of middle-aged women that reflects the trend of the future should be continued.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.603-613
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• 2017

Many people have recently posted about personal interests on social media. The development of the Internet and computer technology has enabled the storage of digital forms of documents that has resulted in an explosion of the amount of textual data generated; subsequently there is an increased demand for technology to create valuable information from a large number of documents. A text mining technique is often used since text-based data is mostly composed of unstructured forms that are not suitable for the application of statistical analysis or data mining techniques. This study analyzed the Meteorological Yearbook data of the Korea Meteorological Administration (KMA) with a text mining technique. First, a term dictionary was constructed through preprocessing and a term-document matrix was generated. This term dictionary was then used to calculate the annual frequency of term, and observe the change in relative frequency for frequently appearing words. We also used regression analysis to identify terms with increasing and decreasing trends. We analyzed the trends in the Meteorological Yearbook of the KMA and analyzed trends of weather related news, weather status, and status of work trends that the KMA focused on. This study is to provide useful information that can help analyze and improve the meteorological services and reflect meteorological policy.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Journal of Korean Home Economics Education Association
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• v.27 no.4
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• pp.37-50
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• 2015

The purpose of this study was to analyze the core content of consumer area from 1st to 2009 revised middle school Home Economics textbooks with the aim of proposing guidelines. An in-depth content analysis was conducted twenty two Home Economics textbooks that have been used in middle schools, beginning with the first curriculum until the revised 2009 curriculum with word cloud. The major findings of this study were as follows; First, the first period of the textbooks emphasized thrift-related concepts such as budgeting and saving money. The second and third period in Home Economics textbooks focused on household work and resource management. From fifth period, the content of textbooks were emphasized learning how to find relevant information and making rational decisions as a consumer. The 2007 revised and 2009 revised period in Home Economics have focused on rational decision-making by adolescents, while taking into account environmental considerations. The content of textbooks now introduces students to the notions of ethical consumerism and eco-friendly consumption across domains such as clothing, food, and housing. The curriculum and content of textbooks in Home Economics should emphasize the concepts related to ethical consumption.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.7
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• pp.371-378
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• 2017

Recently, with the spread of smart device and the introduction of web services, the data is rapidly increasing online, and it is utilized in various fields. In particular, the emergence of social media in the big data field has led to a rapid increase in the amount of unstructured data. In order to extract meaningful information from such unstructured data, interest in big data technology has increased in various fields. Big data is becoming a key resource in many areas. Big data's prospects for the future are positive, but concerns about data breaches and privacy are constantly being addressed. On this subject of big data, where positive and negative views coexist, the research of analyzing people's opinions currently lack. In this study, we compared the changes in peoples perception on big data based on unstructured data collected from the social media using a text mining. As a results, yearly keywords for domestic big data, declining positive opinions, and increasing negative opinions were observed. Based on these results, we could predict the flow of domestic big data.