• Journal of Digital Convergence
• /
• v.19 no.12
• /
• pp.359-367
• /
• 2021

The purpose of this study is to analyze domestic patient safety culture research topics using text mining and CONCOR analysis. The research method was conducted in the stages of data collection, data preprocessing, text mining and social network analysis, and CONCOR analysis. A total of 136 articles were analyzed excluding papers that were not published. Data analysis was performed using Textom and UCINET programs. As a result of this study, TF (frequency) of patient safety culture-related studies showed that patient safety was the highest, and TF-IDF (importance in documents) was highest in nursing. As a result of the CONCOR analysis, a total of seven clusters were derived: knowledge and attitude, communication, medical service, team, work environment, structure, organization and management that constitute the patient safety culture. In the future, it is necessary to conduct research on the relationship between the establishment of a patient safety culture and patient outcomes.

• Journal of the Korea Convergence Society
• /
• v.13 no.2
• /
• pp.1-12
• /
• 2022

The purpose of this study is to identify the social perception characteristics of young children with disabilities over the past decade. For this purpose, Textom, an Internet-based big data analysis system was used to collect data related to young children with disabilities posted on social media. 50 keywords were selected in the order of high frequency through the data cleaning process. For semantic network analysis, centrality analysis and CONCOR analysis were performed with UCINET6, and the analyzed data were visualized using NetDraw. As a result, the keywords such as 'education, needs, parents, and inclusion' ranked high in frequency, degree, and eigenvector centrality. In addition, the keywords of 'parent, teacher, problem, program, and counseling' ranked high in betweenness centrality. In CONCOR analysis, four clusters were formed centered on the keywords of 'disabilities, young child, diagnosis, and programs'. Based on these research results, the topics on social perception of young children with disabilities were investigated, and implications for each topic were discussed.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.8
• /
• pp.137-144
• /
• 2023

One of the methods being suggested as a way to address social issues is the utilization of big data analysis techniques. In this study, we utilized keyword network analysis and CONCOR analysis techniques to analyze the research trends on work-related musculoskeletal disorders. The findings of this study are as follows: Firstly, the number of papers on work-related musculoskeletal disorders has been consistently increasing, with an average of over 33 articles published per year since the investigation of musculoskeletal risk factors in 2003. The publication rate showed an increase from 2007 to 2009. Secondly, the frequency of the top keywords identified through text mining were as follows: work (4,940), musculoskeletal disorders (2,197), symptoms (1,836), related (1,769), musculoskeletal system (1,421). Thirdly, the CONCOR analysis resulted in the formation of four clusters: ' Musculoskeletal disorder treatment', 'Occupational health and safety management', 'Work environment assessment', and ' Workplace environment measurement'. It is expected that this study will contribute to the development of research on musculoskeletal disorders and provide various directions for future studies.

• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.187-192
• /
• 2023

In this study, trends in ICT education were investigated by analyzing the frequency of appearance of keywords related to machine learning and using conversion of iteration correction(CONCOR) techniques. A total of 304 papers from 2018 to the present published in registered sites were searched on Google Scalar using "ICT education" as the keyword, and 60 papers pertaining to ICT education were selected based on a systematic literature review. Subsequently, keywords were extracted based on the title and summary of the paper. For word frequency and indicator data, 49 keywords with high appearance frequency were extracted by analyzing frequency, via the term frequency-inverse document frequency technique in natural language processing, and words with simultaneous appearance frequency. The relationship degree was verified by analyzing the connection structure and centrality of the connection degree between words, and a cluster composed of words with similarity was derived via CONCOR analysis. First, "education," "research," "result," "utilization," and "analysis" were analyzed as main keywords. Second, by analyzing an N-GRAM network graph with "education" as the keyword, "curriculum" and "utilization" were shown to exhibit the highest correlation level. Third, by conducting a cluster analysis with "education" as the keyword, five groups were formed: "curriculum," "programming," "student," "improvement," and "information." These results indicate that practical research necessary for ICT education can be conducted by analyzing ICT education trends and identifying trends.

• Fashion & Textile Research Journal
• /
• v.25 no.5
• /
• pp.549-559
• /
• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.305-313
• /
• 2021

This study collected and analyzed big data from 1995 to 2020, focusing on the keywords "unwed mother", "single mother," and "single mom" to present appropriate government support policy directions according to changes in perspectives on unwed mothers. Big data collection platform Textom was used to collect data from portal search sites Naver and Daum and refine data. The final refined data were word frequency analysis, TF-IDF analysis, an N-gram analysis provided by Textom. In addition, Network analysis and CONCOR analysis were conducted through the UCINET6 program. As a result of the study, similar words appeared in word frequency analysis and TF-IDF analysis, but they differed by year. In the N-gram analysis, there were similarities in word appearance, but there were many differences in frequency and form of words appearing in series. As a result of CONCOR analysis, it was found that different clusters were formed by year. This study confirms the change in the perspective of unwed mothers through big data analysis, suggests the need for unwed mothers policies for various options for independent women, and policies that embrace pregnancy, childbirth, and parenting without discrimination within the new family form.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.99-110
• /
• 2023

This study analyzed 817 articles published in Korean professional journals over the past three years, from 2020 to 2022, using text mining techniques to identify trends in addiction research in Korea and explore development directions. The analysis results are as follows. First, as a result of the analysis of the top keywords, online addiction studies such as smartphones, games, Internet, gambling, and relationship addiction were prominent as the top keywords. Second, as a result of TF-IDF analysis, many addiction studies related to behavioral addiction such as smartphones, games, the Internet, and work addiction have been conducted over the past three years, and in particular, there are many studies on addiction problems such as smartphones, games, and the Internet that have not yet been clinically diagnosed as addiction problems. This is the same as the result of word frequency analysis, and it can be interpreted that recent studies have been remarkably conducted on more diverse addiction problems. Third, the 2-gram analysis shows that words that mainly correspond to behavioral addiction, such as smartphones, games, and the Internet, appear side by side with the keyword addiction, and among them, words paired with smartphones are mentioned a lot in research papers and are being studied. Fourth, as a result of the CONCOR analysis, there were five clusters: a study on universal addiction issues such as alcohol use disorders and the Internet, a study of recovery on drug and gambling addiction, a study on mobile devices and media addiction, a study on the latest trends related to behavioral addiction, and other addiction issues. Finally, based on the results of this study, a direction for future addiction-related research was suggested.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.1
• /
• pp.222-230
• /
• 2023

The purpose of this study is to provide basic data on sports entertainment programs by collecting data on unstructured data generated by Naver and Google for SBS entertainment program 'Women Who Score Goal', which began regular broadcast in June 2021, and analyzing public perceptions through data mining, semantic matrix, and CONCOR analysis. Data collection was conducted using Textom, and 27,911 cases of data accumulated for 16 months from June 16, 2021 to October 15, 2022. For the collected data, 80 key keywords related to 'Kick a Goal' were derived through simple frequency and TF-IDF analysis through data mining. Semantic network analysis was conducted to analyze the relationship between the top 80 keywords analyzed through this process. The centrality was derived through the UCINET 6.0 program using NetDraw of UCINET 6.0, understanding the characteristics of the network, and visualizing the connection relationship between keywords to express it clearly. CONCOR analysis was conducted to derive a cluster of words with similar characteristics based on the semantic network. As a result of the analysis, it was analyzed as a 'program' cluster related to the broadcast content of 'Kick a Goal' and a 'Soccer' cluster, a sports event of 'Kick a Goal'. In addition to the scenes about the game of the cast, it was analyzed as an 'Everyday Life' cluster about training and daily life, and a cluster about 'Broadcast Manipulation' that disappointed viewers with manipulation of the game content.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.3
• /
• pp.116-130
• /
• 2019

This study investigates passenger-authored online reviews of airline services using social network analysis to compare the differences in customer perceptions between full service carriers (FSCs) and low cost carriers (LCCs). While deriving words with high frequency and weight matrix based on the text analysis for FSCs and LCCs respectively, we analyze the semantic network (betweenness centrality, eigenvector centrality, degree centrality) to compare the degree of connection between words in online reviews of each airline types using the social network analysis. Then we compare the words with high frequency and the connection degree to gauge their influences in the network. Moreover, we group eight clusters for FSCs and LCCs using the convergence of iterated correlations (CONCOR) analysis. Using the resultant clusters, we match the clusters to dimensions of two types of service quality models ($Gr{\ddot{o}}nroos$, Brady & Cronin (B&C)) to compare the airline service quality and determine which model fits better. From the semantic network analysis, FSCs are mainly related to inflight service words and LCCs are primarily related to the ground service words. The CONCOR analysis reveals that FSCs are mainly related to the dimension of outcome quality in $Gr{\ddot{o}}nroos$ model, but evenly distributed to the dimensions in B&C model. On the other hand, LCCs are primarily related to the dimensions of process quality in both $Gr{\ddot{o}}nroos$ and B&C models. From the CONCOR analysis, we also observe that B&C model fits better than $Gr{\ddot{o}}nroos$ model for the airline service because the former model can capture passenger perceptions more specifically than the latter model can.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.24 no.3
• /
• pp.1-15
• /
• 2022

The purpose of this study is to compare and analyze the perception of men's fashion before and after the COVID-19 pandemic. TEXTOM allowed the collection of Big Data based on the term 'men's fashion'. As for the data collection periods, Jan. 1, 2018 to Dec. 31, 2019 was set as the pre-COVID-19 era, while Jan. 1, 2020 to Dec. 31, 2021 was set as the post-COVID-19 era. The top 50 words in terms of appearance frequency were extracted from the data. The extracted words were processed using network centrality analysis and CONCOR analysis using Ucinet 6. Research findings were as follows. 1) In the pre-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'men's fashion', 'brand', 'daily look', 'suit', and 'department store'. These words came up with a high TF-IDF values. Network centrality analysis discovered that 'men', 'fashion', 'men's fashion', 'brand', and 'suit' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and styles', 'fashion show', 'purchase', and 'collection'. 2) In the post-COVID-19 era, the appearance frequency of 'men' was the highest, followed by 'fashion', 'brand', 'men's fashion', 'discount', 'women', and 'luxury'. These words also displayed high TF-IDF values. Network centrality analysis found that 'fashion', 'men', 'brand', 'men's fashion', and 'discount' had a high level of connectivity with other words. CONCOR analysis showed four significant groups: 'fashion item and style', 'fashion show', 'purchase', and 'situation'. 3) Before the outbreak of the pandemic, men were interested in suits to wear to the office, daily look, and fashion shows in Milan and Paris. They often purchased menswear in multi-brand and open stores. However, they were more interested in sneakers, casual styles, and online fashion shows as social distancing and working from home became common. Most purchased menswear through online platforms.