• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.2
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• pp.85-108
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• 2018

This study aimed to analyze media reports of the Comprehensive Library Advancement Plan using big data analysis in order to determine trends and implications by period. To do so, related data from 2009 to 2017 were collected from major domestic web portal sites. Words in the collected data were refined through the text mining process and frequency, centrality, and structural equivalence analyses were performed. Results confirmed that, during the implementation of the first and the second phases of the Comprehensive Library Advancement Plan, the focus of the library policy changed from external growth to strengthening internal stability and advancement of library operation, and the media coverage were limited to specific policies such as expansion of library facilities. Findings from this study will serve as useful material for ascertaining the approach to perceive and understand the national library policy represented by the Comprehensive Library Advancement Plan.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.311-320
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• 2023

This paper analyzes viewers' perception of investigative journalism before and after COVID-19, and examines the direction of investigative journalism using big data. Based on the previous research set as a social science model, the relationship between words related to big data TV current affairs programs and investigative journalism in this paper was investigated before and after the appearance of COVID-19. We visualized changes in viewers' perception of investigative journalism by analyzing text data obtained through the use of Textom, with TV current affairs programs and investigative journalism as keywords. Data was collected from 2017 to June 2022 and refined for analysis. We visualized connectivity centrality using Ucinet 6.0 and Netdraw, and clustered the number of keywords and their frequency using Concor analysis. Our study found a clear change in viewer perception before and after the pandemic. As an implication of this thesis, big data analysis was conducted with the investigative journalism as the main keyword, and the direction of the investigative journalism was presented based on the analysis. Furthermore, based on previous research, we suggest effective approaches for investigative journalism after the pandemic to better engage viewers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.354-360
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• 2021

People hope to live a healthy and happy life achieving satisfaction by striking a good work-life balance. Therefore, there is a growing interest in well-aging which means living happily to a healthy old age without worry. This study identified important factors related to well-aging by analyzing news articles published in Korea. Using Python-based web crawling, 1,199 articles were collected on the news service of portal site Daum till November 2020, and 374 articles were selected which matched the subject of the study. The frequency analysis results of text mining showed keywords such as 'elderly', 'health', 'skin', 'well-aging', 'product', 'person', 'aging', 'female', 'domestic' and 'retirement' as important keywords. Besides, a social network analysis with 45 important keywords revealed strong connections in the order of 'skin-wrinkle', 'skin-aging' and 'old-health'. The result of the CONCOR analysis showed that 45 main keywords were composed of eight clusters of 'life and happiness', 'disease and death', 'nutrition and exercise', 'healing', 'health', and 'elderly services'.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.167-181
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• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.308-321
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• 2021

The purpose of this study was to analyze domestic research trends over the past 20 years and future direction of forest ecology using text mining. A total of 1,015 academic papers and keywords data related to forest ecology were collected by the "Research and Information Service Section" and analyzed using big data analysis programs, such as Textom and UCINET. From the results of word frequency and N-gram analyses, we found domestic studies on forest ecology rapidly increased since 2011. The most common research topic was "species diversity" over the past 20 years and "climate change" became a major topic since 2011. Based on CONCOR analysis, study subjects were grouped intoeight categories, such as "species diversity," "environmental policy," "climate change," "management," "plant taxonomy," "habitat suitability index," "vascular plants," and "recreation and welfare." Consequently, species diversity and climate change will remain important topics in the future and diversifying and expanding domestic research topics following global research trendsis necessary.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.724-732
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• 2023

In this study, we tried to analyze the characteristics of color cosmetics information search and the major information of interest in the color cosmetics market after COVID-19 shown in the text mining analysis results by collecting data on online interest information of consumers in the color cosmetics market after COVID-19. In the empirical analysis, text mining was performed on all documents such as news, blogs, cafes, and web pages, including the word "color cosmetics". As a result of the analysis, online information searches for color cosmetics after COVID-19 were mainly focused on purchase information, information on skin and mask-related makeup methods, and major topics such as interest brands and event information. As a result, post-COVID-19 color cosmetics buyers will become more sensitive to purchase information such as product value, safety, price benefits, and store information through active online information search, so a response strategy is required.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.543-550
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• 2019

In 2006, Janet Wing defined computational thinking and operated SW education as a formal curriculum in the UK in 2013. This study collected related research papers by using computational thinking, which has recently increased in importance, and analyzed it using text mining. In the first, CONCOR analysis was conducted with the keyword of computational thinking. In the second, text mining of the components of computational thinking was selected by the repr23esentative academic journals at domestic and foreign. As a result of the two-time analysis, first, abstraction, algorithm, data processing, problem decomposition, and pattern recognition were the core of the study of computational thinking component. Second, research on convergence education centered on computational thinking and science and mathematics subjects was actively conducted. Third, research on computational thinking has been expanding since 2010. Research and development of the classification and definition of computational thinking and components and applying them to education sites should be conducted steadily.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.101-114
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• 2023

This research aimed to recognize the determinant characteristics affecting consumers' clothing fit evaluation by employing text mining and social network analysis. For this aim, we first extracted text data linked to clothing fit from 2,000 consumer reviews collected from social network services and conducted semantic network examination and CONCOR analysis. As a result, we reported that "pants" and "skirts" were the most commonly associated clothing items with consumers' clothing fit evaluation. And the length of clothing was most commonly investigated. Then, the "waist" and "hip" were the most critical body parts affecting consumers' perception of clothing fit. Further, the four keywords including "wide," "large," "short," and "long" were the most employed ones in consumer reviews when evaluating clothing fit. This study is meaningful in that it specifically recognized the structural relationship and semantic meanings of keywords relevant to consumers' evaluation of clothing fit, which could bring empirical reference information for advanced clothing fit.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.639-649
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• 2020

This study investigates consumer opinions of clothing care and provides fundamental data to decision-making for oncoming development of clothing care system. Textom, a web-matrix program, was used to analyze big data collected from Naver and Daum with a keyword of "clothing care" from March 2019 to February 2020. A total of 22, 187 texts were shown from the big data collection. Collected big data were analyzed using text-mining, network, and CONCOR analysis. The results of this study were as follows. First, many keywords related to clothing care were shown from the result of frequency analysis such as style, Dryer, LG Electronics, Product, Customer, Clothing, and Styler. Consumers were well recognizing and having an interest in recent information related to the clothing care system. Second, various keywords such as product, function, brand, and performance, were linked to each other which were fundamentally related to the clothing care. The interest in products of the clothing care system were linked to product brands that were also naturally linked to consumer interest. Third, the keywords in the network showed similar attributes from the result of CONCOR analysis that were classified into 4 groups such as the characteristics of purchase, product, performance, and interest. Lastly, positive emotions including goodwill, interest, and joy on the clothing care system were strongly expressed from the result of the sentimental analysis.

• Journal of Korean Clinical Nursing Research
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• v.25 no.3
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• pp.209-220
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• 2019

Purpose: A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis. Methods: From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as "nurse", "Taeum", and "bullying" were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence. Results: This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, "nurse", "hospital", "patient", and "physician" were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters. Conclusion: This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.