• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.137-144
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• 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 Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.17-29
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• 2024

The Gyeongin Ara Waterway is a regional development project designed to transport cargo by sea and to utilize the surrounding waterfront area to enjoy tourism and leisure. It is being used as a space for demonstration projects for urban air transportation (UAM), which has recently been attracting attention, and various efforts are being made at the local level to strengthen cultural and tourism functions and revitalize local food. This study examined the perception and trends of tourism consumers on the Gyeongin Ara Waterway before and after the outbreak of COVID-19. The research method utilized semantic network analysis based on social network analysis. As a result of the study, first, before the outbreak of COVID-19, key words such as bicycle, Han River, riding, Gimpo, Seoul, hotel, cruise ship, Korea Water Resources Corporation, emotion, West Sea, weekend, and travel showed a high frequency of appearance. After the outbreak of COVID-19, keywords such as cafe, discovery, women, Gimpo, restaurant, bakery, observatory, La Mer, and cruise ship showed a high frequency of appearance. Second, the results of the degree centrality analysis showed that before the outbreak of COVID-19, there was increased interest in accommodations for tourism, such as Marina Bay and hotels. After the outbreak of COVID-19, interest in food such as specific bakeries and cafes such as La Mer was found to be high. Third, due to the CONCOR analysis, five keyword clusters were formed before the outbreak of COVID-19, and the number of keyword clusters increased to eight after the outbreak of COVID-19.

• Journal of People, Plants, and Environment
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• v.24 no.4
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• pp.415-427
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• 2021

Background and objective: This study examined the changes in the cultural trend of use for green infrastructure in Seoul due to COVID-19 pandemic. Methods: The subjects of this study are 8 sites of green infrastructure selected by type: Forested green infrastructure, Watershed green infrastructure, Park green infrastructure, Walkway green infrastructure. The data used for analysis was blog posts for a total of four years from August 1, 2016 to July 31, 2020. The analysis method was conducted keyword frequency analysis, topic modeling, and related keyword analysis. Results: The results of this study are as follows. First, the number of posts on green infrastructure has increased since COVID-19, especially forested green infrastructure and watershed green infrastructure with abundant naturalness and high openness. Second, the cultural trend keywords before and after COVID-19 changed from large-scale to small-scale, community-based to individual-based activities, and nondaily to daily culture. Third, after COVID-19, topics and keywords related to coronavirus showed that the cultural trends were reflected on appreciation, activities, and dailiness based on natural resources. In sum, the interest in green infrastructure in Seoul has increased after COVID-19. Also, the change of green infrastructure represents the increased demand for experience that reflects the need and expectation for nature. Conclusion: The new trend of green Infrastructure in the pandemic era should be considered in the the individual relaxations & activities.

• The Journal of Information Systems
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• v.27 no.2
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• pp.1-28
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• 2018

Purpose More than 7.6 million mobile apps could be approved on both Apple iTunes Store and Google Play. For managing those existed Apps, Apple Inc. established twenty-four primary categories, as well as Google Play had thirty-three primary categories. However, all of their categorizations have appeared more and more problems in managing and classifying numerous apps, such as app miscategorized, cross-attribution problems, lack of categorization keywords index, etc. The purpose of this study focused on introducing individual information cognitive processing as the classification criteria to update the current categorization on Apple iTunes Store. Meanwhile, we tried to observe the effectiveness of the new criteria from a classification process on Apple iTunes Store. Design/Methodology/Approach A research approach with four research stages were performed and a series of mixed methods was developed to identify the feasibility of adopting individual information cognitive processing as categorization criteria. By using machine-learning techniques with Term Frequency-Inverse Document Frequency and Singular Value Decomposition, keyword lists were extracted. By using the prior research results related to car app's categorization, we developed individual information cognitive processing. Further keywords extracting process from the extracted keyword lists was performed. Findings By TF-IDF and SVD, keyword lists from more than five thousand apps were extracted. Furthermore, we developed individual information cognitive processing that included a categorization teaching process and learning process. Three top three keywords for each category were extracted. By comparing the extracted results with prior studies, the inter-rater reliability for two different methods shows significant reliable, which proved the individual information cognitive processing to be reliable as criteria of categorization on Apple iTunes Store. The updating suggestions for Apple iTunes Store were discussed in this paper and the results of this paper may be useful for app store hosts to improve the current categorizations on app stores as well as increasing the efficiency of app discovering and locating process for both app developers and users.

• Journal of Distribution Science
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• v.20 no.3
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• pp.59-69
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• 2022

Purpose: The purpose of this study is to provide information for other journals as well as the continuous development of distribution science research by confirming the globalization performance of the Journal of Distribution Science (JDS), the main journal of KODISA. Research Design, Data, and Methodology: A total of 863 papers published in JDS from 2011 to 2021 searched by scienceON were divided into 4 periods and analyzed under the headings of submission system, standardity, collaboration, and degree of achievement of publication goals. SPSS 24.0 and R 4.1.1 package were used to perform the publication frequency analysis, crosstab-analysis, keyword frequency analysis, and LDA topic modeling were performed. In addition, trend analysis with weight applied to each word was performed. Results: It was found that the ratio of English-written papers, which is the indicator of a journal's starndardity, is continuously increasing, and the ratio of overseas authors, which is the indicator of collaboration, is also continuously increasing. It was confirmed through keyword trend analysis by period and LDA topic modeling results - which were weighted to confirm the degree of achievement of the journal's publication goal - that the articles published by the journal has been in agreement with monthly research topic proposed by JDS. Conclusion: By examining the five criteria for globalization, it can be concluded that JDS's efforts for globalization are achieving significant results and providing effective directions for other academic journals. However, in order for JDS to become a top academic journal, it was suggested that efforts should be made to establish a system for collaborative research by domestic and foreign authors, as well as to provide a clear definition for the monthly research topics and classification of sub-topics.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.53-63
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• 2020

Electronic financial transactions are also actively increasing due to the rapid spread of information communication media such as the Internet, smart devices, and IoT, but as a derivative by-product, threats of financial security such as leakage of various personal information and hacking are also increasing. Therefore, the importance of financial security against this is increasing, but in Korea, financial security technology is relatively insufficient compared to advanced countries in the field of financial security, such as Active-X. Therefore, this study aims to present the major development direction in the domestic financial security field by comparing key technology trends with IPC classification frequency analysis, keyword frequency analysis, and keyword network analysis based on domestic and foreign financial security-related patent data. In conclusion, it seems that recent domestic and foreign trends have focused on the development of related technologies according to the development of smart device-based electronic financial services. Accordingly, it is intended to be used as the basis data for technology development of financial security by mapping the trend of financial security research trend and technology trend analysis through thesis data analysis that reflects the research of the preceding aspect as the technology of commercialization in the future.

• The Journal of Economics, Marketing and Management
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• v.10 no.6
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• pp.1-7
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• 2022

Purpose: The purpose of this study is identifying the research trends of degree papers related to the beauty industry and providing information which can contribute to the development of the domestic beauty industry and the direction of various research about beauty industry. Research design, data and methodology: This study used 154 academic papers and 189 academic papers with English abstracts out of 299 academic papers. All of these papers were found by searching for the keyword "beauty industry" in ScienceON on August 15, 2022. For the analysis, BERTopic and LDA (Latent Dirichlet Allocation) analysis were conducted using Python 3.7. Also, OLS regression analysis was conducted to understand the annual increase and decrease trend of each topic derived with trend analysis. Results: As a result of word frequency analysis, the frequency of satisfaction, management, behavior, and service was found to be high. In addition, it was found that 'service', 'satisfaction' and 'customer' were frequently associated with program and relationship in the word co-occurrence frequency analysis. As a result of topic modeling, six topics were derived: 'Beauty shop', 'Health education', 'Cosmetics', 'Customer satisfaction', 'Beauty education', and 'Beauty business'. The trend analysis result of each topic confirmed that 'Beauty education' and 'Health education' are getting more attention as time goes by. Conclusions: The future studies must resolve the extreme polarization between the structure of the small beauty industry and beauty stores. Furthermore, the researches have to direct various ways to create the performance of internal personnel. The ways to maximize product capabilities such as competitive cosmetics and brands are also needed attentions.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.38-49
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• 2021

This study analyzed the trend of the term 'false and exaggerated advertisement' in 5,141 newspaper articles from 1990 to 2019 using text mining methodology. First of all, we identified the most frequent keywords of false and exaggerated advertisements through frequency analysis for all newspaper articles, and understood the context between the extracted keywords. Next, to examine how false and exaggerated advertisements have changed, the frequency analysis was performed by separating articles by 10 years, and the tendency of the keyword that became an issue was identified by comparing the number of academic papers on the subject of the highest keywords of each year. Finally, we identified trends in false and exaggerated advertisements based on the detailed keywords in the topic using the topic modeling. In our results, it was confirmed that the topic that became an issue at a specific time was extracted as the frequent keywords, and the keyword trends by period changed in connection with social and environmental factors. This study is meaningful in helping consumers spend wisely by cultivating background knowledge about unfair advertising. Furthermore, it is expected that the core keyword extraction will provide the true purpose of advertising and deliver its implications to companies and related employees who commit misconduct.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.19-30
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• 2023

This study aims to analyze service improvement and success factors of electric scooter sharing service companies by using text mining after collecting reviews of shared electric scooter service applications among various models of sharing economy. In this study, the factors of satisfaction and dissatisfaction of service users were identified using the term frequency inverse document frequency (TF-IDF) technique, and topics for each keyword were extracted using the Latent Dirichlet Allocation (LDA) Topic Modeling technique. According to the analysis results, the main topics were entertainment, safety, service area, application complaints, use complaints, convenience, and mobility. Using the analysis results of this study, employees and researchers of electric scooter sharing service companies will be able to contribute to the improvement and success of related services.

• The Korean Journal of Food & Health Convergence
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• v.9 no.1
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• pp.19-28
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• 2023

The purpose of this study is to explore which areas have been more interested in depression research in Korea through analysis of academic papers related to depression, and then to provide insights that can solve future depression problems. 1,032 papers searched with the keyword "depression" in scienceON were analyzed using Python 3.7 for word frequency analysis, word co-occurrence analysis, BERTopic, LDA, and OLS regression analysis. The results of word frequency and co-occurrence frequency analysis showed that related words were composed around words such as patient, disorder and symptom. As a result of topic modeling, a total of 13 topics including 'childhood depression' and 'eating anxiety' were derived. And it has been identified as a topic of interest that 'suicidal thoughts', 'treatment', 'occupational health', and 'health treatment program' were statistically significant topics, while 'child depression' and 'female treatment' were relatively less. As a result of the analysis of research trends, future research will not only study physiological and psychological factors but also social and environmental causes, as well as it was suggested that various collaborative studies of experts in academia were needed such as convergence and complex perspectives for depression relief and treatment.