• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.79-96
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• 2021

Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.11-21
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• 2022

All-solid-state batteries are one of the promising candidates for next-generation batteries and are drawing attention as a key component that will lead the future electric vehicle industry. This study analyzes 10,280 comments on Reddit, which is a global social media, in order to identify policy issues and public interest related to all-solid-state batteries from 2016 to 2021. Text mining such as frequency analysis, association rule analysis, and topic modeling, and sentiment analysis are applied to the collected global data to grasp global trends, compare them with the South Korean government's all-solid-state battery development strategy, and suggest policy directions for its national research and development. As a result, the overall sentiment toward all-solid-state battery issues was positive with 50.5% positive and 39.5% negative comments. In addition, as a result of analyzing detailed emotions, it was found that the public had trust and expectation for all-solid-state batteries. However, feelings of concern about unresolved problems coexisted. This study has an academic and practical contribution in that it presented a text mining analysis method for deriving key issues related to all-solid-state batteries, and a more comprehensive trend analysis by employing both a top-down approach based on government policy analysis and a bottom-up approach that analyzes public perception.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.393-413
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• 2021

Digital humanities, which creates new and innovative knowledge through the combination of digital information technology and humanities research problems, can be seen as a representative multidisciplinary field of study. To investigate the intellectual structure of the digital humanities field, a network analysis of authors and keywords co-word was performed on a total of 441 papers in the last two years (2019, 2020) at the Digital Humanities Conference. As the results of the author and keyword analysis show, we can find out the active activities of Europe, North America, and Japanese and Chinese authors in East Asia. Through the co-author network, 11 dis-connected sub-networks are identified, which can be seen as a result of closed co-authoring activities. Through keyword analysis, 16 sub-subject areas are identified, which are machine learning, pedagogy, metadata, topic modeling, stylometry, cultural heritage, network, digital archive, natural language processing, digital library, twitter, drama, big data, neural network, virtual reality, and ethics. This results imply that a diver variety of digital information technologies are playing a major role in the digital humanities. In addition, keywords with high frequency can be classified into humanities-based keywords, digital information technology-based keywords, and convergence keywords. The dynamics of the growth and development of digital humanities can represented in these combinations of keywords.

• Informatization Policy
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• v.23 no.3
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• pp.26-42
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• 2016

Recently, with the rapid growth of the O2O market, Fintech combining the finance and ICT technology is drawing attention as innovation to lead "O2O of finance", along with Fintech-based payment, authentication, security technology and related services. For new technology industries such as Fintech, technical sources, related systems and regulations are important but previous studies on Fintech lack in-depth research about systems and technological trends of the domestic Fintech industry. Therefore, this study aims to analyze domestic Fintech trends and find the insights for the direction of technology and systems of the future domestic Fintech industry by comparing Kakao Pay and Samsung Pay, the two domestic representative mobile payment services. By conducting a complete enumeration survey about the tweets mentioning Fintech until June 2016, this study visualized topics extraction, sensitivity analysis and keyword analyses. According to the analysis results, it was found that various topics have been created in the technologies and systems between 2014 and 2016 and different keywords and reactions were extracted between topics of Samsung Pay based on "devices" such as Galaxy and Kakao Pay based on "service" such as KakaoTalk. This study contributes to analyzing the unstructured data of social media by period by using social media mining and quantifying the expectations and reactions of consumers to services through the sentiment analysis. It is expected to be the foundation of Fintech industry development by presenting a strategic direction to Fintech related practitioners.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.549-566
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• 2022

Recently, artificial intelligence (AI) technology has progressed rapidly, and industries using this technology are significantly increasing. Further, analysis research using text mining, which is an application of artificial intelligence, is being actively developed in the field of social science research. About 125 laws, including joint laws, have been enacted under the Ministry of Oceans and Fisheries in various sectors including marine environment, fisheries, ships, fishing villages, ports, etc. Research on the laws under the Ministry of Oceans and Fisheries has been progressively conducted, and is steadily increasing quantitatively. In this study, the domestic research trends were analyzed through text mining, targeting the research papers related to laws of the Ministry of Oceans and Fisheries. As part of this research method, first, topic modeling which is a type of text mining was performed to identify potential topics. Second, co-occurrence network analysis was performed, focusing on the keywords in the research papers dealing with specific laws to derive the key themes covered. Finally, author network analysis was performed to explore social networks among authors. The results showed that key topics have been changed by period, and subjects were explored by targeting Ship Safety Law, Marine Environment Management Law, Fisheries Law, etc. Furthermore, in this study, core researchers were selected based on author network analysis, and the tendency for joint research performed by authors was identified. Through this study, changes in the topics for research related to the laws of the Ministry of Oceans and Fisheries were identified up to date, and it is expected that future research topics will be further diversified, and there will be growth of quantitative and qualitative research in the field of oceans and fisheries.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.669-676
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• 2006

Hydrological factors, especially the spatial distribution of interpretation on precipitation is often topic of interest in studying of water resource. The popular methods such as Thiessen method, inverse distance method, and isohyetal method are limited in calculating the spatial continuity and geographical characteristics. This study was intended to overcome those limitations with improved method that will yield higher accuracy. The monthly and yearly precipitation data were produced and compared with the observed daily precipitation to find correlation between them. They were then used as secondary variables in Co-kriging method, and the result was compared with the outcome of existing methods like inverse distance method and kriging method. The comparison of the data showed that the daily precipitation had high correlation with corresponding year's average monthly amounts of precipitation and the observed average monthly amounts of precipitation. Then the result from the application of these data for a Co-kriging method confirmed increased accuracy in the modeling of spatial distribution of precipitation, thus indirectly reducing inconsistency of the spatial distribution of hydrological factors other than precipitation.

• The Korean Journal of Franchise Management
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• v.13 no.3
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• pp.31-43
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• 2022

Purpose: Service quality is a topic of constant interest in marketing research and practitioners. Service quality is an important factor influencing performance even in the context of coffee shops, and research on service quality management strategies continues by coffee shop researchers and practitioners. The service quality of coffee shops is a source of competitive advantage and is an important factor in enhancing customer and business performance. This study aims to identify the effects of cognitive evaluation on emotional and behavioral responses using a cognitive-emotional-behavioral framework and SOR model in the coffee shop context. Cognitive evaluation (service quality) consists of tangibles, responsiveness, assurance, reliability, and empathy dimensions. Research design, data, and methodology: In the proposed model, positive and negative emotions and satisfaction mediate the relationship between service quality and money to spend and visit frequency. The data were collected from customers who visited a coffee shop within the last 1 month. The survey was conducted for about one month. Among a total of 300 distributed questionnaires 261 responses were used for data analysis. The data were analyzed using frequency analysis, measurement model analysis, and structural equation modeling analysis with SPSS 28.0 and SmartPLS 4.0. Results: Tangibles, responsiveness, assurance, and empathy had significant positive effects on positive emotion, while only reliability had a significant negative effect on negative emotion. Both positive and negative emotions had significant positive effects on customer satisfaction, but not on money to spend and visit frequency. Lastly, customer satisfaction had significant positive effects on money to spend and visit frequency. Conclusions: The study revealed the relative weight of cognitive factors on customer emotions and confirmed the validity of SOR model. The fact that tangibility is the most important factor in increasing positive emotions and reliability is the most important factor in reducing negative emotions provides a direction for emotional branding strategies using the service quality mix of coffee shops. This study confirmed the full mediating role of satisfaction between positive and negative emotions and consumer behaviors (money to spend and visit frequency). This infers that when a coffee shop increases customer satisfaction through customer emotion management, the customer's money to spend and visit frequency in the coffee shop increases.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.59-76
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• 2023

This study examines critical issues and topics related to services using YouTube videos and research publications. We analyzed 2,853 YouTube videos and 19,973 research papers related to services, released during the 2013-June, 2023 period, using text mining and network analysis. In addition, the collected data was divided into pre- and post-COVID-19 pandemic periods to explore how key issues and topics regarding services have changed. These papers were sequentially analyzed through text mining and network construction and procedures. The results indicate that the central themes of YouTube videos were IT, data, and solution, while academic research focused on service quality, quality, and customer satisfaction. Regarding ego network analysis, the key issues in YouTube video contents revolved primarily around words related to the service industry. Although it was found that they generally lacked specific industry fields, academic papers explored diverse issues in various service fields. The results of this study can be utilized to understand changes in customer concerns in the service industry from practical and academic perspectives.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.135-161
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• 2023

This study analyzed the research trends of COVID-19 research papers published in medical journals of Korea. Data were collected from 25 MEDLINE journals in 'Medicine and Pharmacy' studies and a total of 800 were selected. As a result of the study, authors from domestic affiliations made up 76.96% of the total, and the proportion of authors from foreign institutions decreased without significant change. The authors' majors were 'Internal Medicine' (32.85%), 'Preventive Medicine/Occupational and Environmental Medicine' (16.23%), 'Radiology' (5.74%), and 'Pediatrics' (5.50%), and 435 (54.38%) papers were collaborative research. As for author keywords, 'COVID19' (674), 'SARSCoV2' (245), 'Coronavirus' (81), and 'Vaccine' (80) were derived as top keywords. There were six words that appeared throughout the entire period: 'COVID19,' 'SARSCoV2,' 'Coronavirus,' 'Korea,' 'Pandemic,' and 'Mortality.' Co-occurrence network analysis was conducted on MeSH terms and author keywords, and common keywords such as 'covid-19,' 'sars-cov-2,' and 'public health' were derived. In topic modeling, five topics were identified, including 'Vaccination,' 'COVID-19 outbreak status,' 'Omicron variant,' 'Mental health, control measures,' and 'Transmission and control in Korea.' Through this study, it was possible to identify the research areas and major keywords by year of COVID-19 research papers published during the 'Public Health Emergency of International Concern (PHEIC).'