• 산업경영시스템학회지
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• 제43권2호
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• pp.14-24
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• 2020

Google Trends is a useful tool not only for setting search periods, but also for providing search volume to specific countries, regions, and cities. Extant research showed that the big data from Google Trends could be used for an on-line market analysis of opinion sensitive products instead of an on-site survey. This study investigated the market share of tumor necrosis factor-alpha (TNF-α) inhibitor, which is in a great demand pharmaceutical product, based on big data analysis provided by Google Trends. In this case study, the consumer interest data from Google Trends were compared to the actual product sales of Top 3 TNF-α inhibitors (Enbrel, Remicade, and Humira). A correlation analysis and relative gap were analyzed by statistical analysis between sales-based market share and interest-based market share. Besides, in the country-specific analysis, three major countries (USA, Germany, and France) were selected for market share analysis for Top 3 TNF-α inhibitors. As a result, significant correlation and similarity were identified by data analysis. In the case of Remicade's biosimilars, the consumer interest in two biosimilar products (Inflectra and Renflexis) increased after the FDA approval. The analytical data showed that Google Trends is a powerful tool for market share estimation for biosimilars. This study is the first investigation in market share analysis for pharmaceutical products using Google Trends big data, and it shows that global and regional market share analysis and estimation are applicable for the interest-sensitive products.

• 웰빙융합연구
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• 제7권4호
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• pp.51-64
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• 2024

Purpose: This study analyzes factors influencing public interest in fermentation using Google search trends. Specifically, it examines how key elements such as oxygen, temperature, time, and pH influence fermentation-0related searches from December 2020 to September 2024. Research design, data and methodology: Data from Google Trends was collected under the Beauty & Fitness category for the terms "Fermentation," "Oxygen," "Temperature," "Time," and "pH." Time series analysis was used to track trends over four years, and a correlation analysis was conducted to assess the relationships between these terms. A linear regression model was built to determine the influence of each factor on fermentation-related searches. The dataset was split into 80% training data and 20% testing data for model validation. Results: The correlation analysis indicated moderate positive relationships between fermentation-related searches and both time and pH, while oxygen had little to no correlation. The regression model showed that time and pH were the strongest influencers of fermentation interest, explaining 25% of the variance (R-squared = 0.25). Oxygen and temperature had minimal impact in predicting fermentation-related search interest. Conclusions: Time and pH are significant factors influencing public interest in fermentation-related topics, as shown by search trends. In contrast, oxygen and temperature, while important in the fermentation process itself, did not strongly affect public search behavior. These findings provide valuable insights for businesses and researchers looking to better understand consumer interest in fermentation products.

• 산업진흥연구
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• 제8권4호
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• pp.251-256
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• 2023

본 연구의 목적은 프로이드의 정신분석 이론을 바탕으로 꿈분석에 관련한 연구가 얼마나 이루어졌는가를 알아 보기 위함이고 꿈의 연구의 필요성과 꿈 연구에 대한 후속 연구를 제언하기 위함이다. 연구방법은 프로이드의 꿈분석에 관한 연구 2019년부터 현재 2023년까지 국내학술지를 중심으로 분석하였다. 그 중에서도 학술연구정보서비스(RISS)와 한국학술지인용색인(KCI)에서 키워드 분류절차를 거쳐 수집하여 정리하였다. 분류범주는 정신분석, 국내학술지, 꿈분석, 꿈해석, 꿈분석 연구동향, 꿈 연구동향 등으로 검색하였고 그 중에서도 특히, 정신분석, 꿈분석, 국내학술지, 연구동향을 중심으로 살펴보았다. 결론은 다음과 같이 도출되었다. 첫째, 국내학술지 내 꿈분석에 관한 연구동향 연구들은 많은 비중을 차지하고 있지는 않았다. 둘째, 꿈분석 키워드 중심 연구동향도 그 비율이 현저하게 낮았다. 셋째, 꿈분석의 활용과 빈도도 적었다. 넷째, 꿈분석을 토대로 한 한국형 검사도구들의 연구가 필요할 것으로 요구된다.

• 한국의류학회지
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• 제46권6호
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• pp.1058-1073
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• 2022

Big data analytics and social media have shifted the way fashion trends are dictated. Fashion as a medium for expressing gender has created new concepts of masculinity in popular culture, where men are increasingly depicted in a softer style. In this study, we analyzed 2,879 menswear collections over a 10-year period from Vogue US to uncover key menswear trends. Using Semantic Network Analysis (SNA) on Orange3, we were able to quantitatively analyze how contemporary menswear designers interpreted diversified trends of masculinity on the runway. Frequency and degree centrality were measured to weigh the significance of trend keywords. "Jacket (f = 3056; DC = 0.80), shirt (f = 1912; DC = 0.60) and pant (f = 1618; DC = 0.53)" were among the most prominent keywords. Our results showed that soft masculine keywords, e.g., "lace, floral, and pink" also appeared, but with the majority scoring DC = < 0.10. The findings provide an insight into key menswear trends through frequency, degree centrality measurements, time-series analysis, egocentric, and visual semantic networks. This also demonstrates the feasibility of using text analytics to visualize design trends, concepts, and patterns for application as an ideation tool for academic researchers, designers, and fashion retailers.

• International Journal of Advanced Culture Technology
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• 제12권2호
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• pp.221-226
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• 2024

This study used text mining, a big data analysis technique, to explore XR trends in South Korea. For this research, I utilized a big data platform called BigKinds. I collected data focusing on the keyword 'XR', spanning approximately 14 years from 2010 to 2024. The gathered data underwent a cleansing process and was analyzed in three ways: keyword trend analysis, relational analysis, and word cloud. The analysis identified the emergence and most active discussion periods of XR, with XR devices and manufacturers emerging as key keywords.

• Asian Journal of Innovation and Policy
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• 제10권3호
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• pp.380-403
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• 2021

Recently, smart factories have gained significant importance since the development of the fourth industrial revolution and the rise of global industrial competition. Therefore, the industries' survival to meet the global market trends requires accurate technological planning. Although, different works are available to investigate forecasting technologies and their influence on the smart factory. However, little significant work is available yet on the analysis of technological trends concerning the smart factory, which is the core focus herein. This work was performed to analyze the technological trends of the smart factory, followed by a detailed investigation of recent research hotspots/frontiers in the field. A well-known topic modeling technique, namely Latent Dirichlet Allocation (LDA), was employed for this study described above. The technological trends were further strengthened with the in-depth analysis of a smart factory-based case study. The findings produced the technological trends which possess significant potential in determining the technological strategies. Moreover, the results of this work may be helpful for researchers and enterprises in forecasting and planning future technological evolution.

• Korean Journal of Acupuncture
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• 제39권1호
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• pp.3-7
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• 2022

Objectives : The study's goal was to look at the research trends of articles published in the Korean Journal of Acupuncture. Methods : The Korean Journal of Acupuncture's website yielded a total of 882 articles. The VOSviewer application was used to visualize research trends from keywords. Results : A total of 87 keywords were found and visualized based on the average year of publication. The relevant characteristics and trends of basic acupuncture research published in Korean Journal of Acupuncture were determined by a network analysis based on the co-occurrences and publication year of keywords. Acupuncture, acupoint, herbal acupuncture, electroacupuncture, moxibustion, and meridian were the most frequently used keywords. Conclusions : This bibliometric study will give you a broad picture of research trends in Korean Journal of Acupuncture. These data may help to establish a timeline for the advancement of acupuncture basic research.

• 아동학회지
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• 제31권5호
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• pp.131-149
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• 2010

The purpose of this study was to examine research trends in articles of preschooler peer relationships carried in domestic academic journals. This was done in an attempt to suggest alternative directions for peer relationship studies in the early childhood education sector and lay the foundation for future studies. 131 articles that appeared in seven domestic academic journals related to early childhood education were selected and analyzed in order to better understand the general trends in the filed and the specific trends in terms of their content and methods. Our results indicate that the observation method was most common in the quantitative studies, and participant observation was most prevailent among qualitative studies. As for instrumentation, international instruments were most widely utilized, and the most dominant analysis method was descriptive statistics. In terms of reliability, internal consistency was checked most often, however, the majority of the studies failed to provide any information on validity and post-hoc analysis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.165-167
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• 2004

End-user electric power consumption trends shows various load curves dependant on industry, contract, season, day and time. Analysis of end-user electric power consumption trends has a key role to efficiently meet electricity demand. There are several factors of change in electricity demand such as the change of weather, international conflict, and industrial trends during summer. This paper has analyzed the analysis the end-user electric power consumption trends using the load curve during international conflict. We observed that international conflict decreased electric demand by $5.4\%$. This increase is not significant, and therefore we conclude that the international conflict has not greatly affected Korea's electricity demands. This paper provides useful information so as to mon: efficiently perform demand side management.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.98-108
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• 2022

In this study, social network analysis was performed to compare and analyze changes in domestic tourism trends before and after the outbreak of COVID-19 in a situation where the damage to the tourism industry due to COVID-19 is increasing. Using Textom, a big data analysis service, data were collected using the keywords "travel destination" and "travel trend" based on the collection period of 2019 and 2020, when the epidemic spread to the world and became chaotic. After extracting a total of 80 key words through text mining, centrality was analyzed using NetDraw of Ucinet6, and clustered into 4 groups through CONCOR analysis. Through this, we compared and analyzed changes in domestic tourism trends before and after the outbreak of COVID-19, and it is judged to provide basic data for tourism marketing strategies and tourism product development in the post-COVID-19.