• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.19-28
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• 2017

Companies and Governments have Mainly used the Delphi Technique to Understand Research or Technology Trends. Because this Technique has the Disadvantage of Consuming a Large Amount of Time and Money, this Study Attempted to Understand and Predict Science and Technology Trends using the Topic Modeling Technique Latent Dirichlet Allocation (LDA). To this end, 20 Specific Artificial Intelligence (AI) Technologies were Extracted From the Abstracts of the US Patent Documents on AI. With Regard to the Extracted Specific Technologies, Core Technologies were Identified, and then these were Divided into Hot and Cold Technologies though a Trend Analysis on their Annual Proportions. Text/Word Searching, Computer Management, Programming Syntax, Network Administration, Multimedia, and Wireless Network Technology were Derived From Hot Technologies. These Technologies are Key Technologies that are Actively Studied in the Field of AI in Recent Years. The Methodology Suggested in this Study may be used to Analyze Trends, Derive Policies, or Predict Technical Demands in Various Fields such as Social Issues, Regional Innovation, and Management.

• Knowledge Management Research
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• v.22 no.3
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• pp.55-73
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• 2021

The sharing economy is considered as a collaborative consumption which enables customers to share unused resources. This study investigated the key factors affecting consumer loyalty in the context of sharing accommodation. Emotions, perceived value and self-image consistency were posited as key antecedents of enhancing customer loyalty. Authentic experience, home amenities, and price fairness were also considered as Airbnb's selection attributes. Airbnb was selected a survey target because it is the largest company in the domain of shared accommodation market. The research model was analyzed for 294 Airbnb customer through structural equation models. Additionally, this paper examine Airbnb customers' experiences by topic modelling method posted on the Naver blog. Based on the understanding of the key factors affecting customer loyalty to sharing accommodation, the analysis results contribute to establish effective marketing and operation strategies by enhancing customer experience.

• Korean Medical Education Review
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• v.24 no.2
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• pp.103-112
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• 2022

The purpose of this study was to explore, using topic modeling, the social value of doctors and medicine demanded by society as reflected in published newspaper articles in Korea. Ultimately, this study aimed to reflect social needs in the process of developing the Patient-Centered Doctor's Competency Framework in Korea. For this purpose, a total of 2,068 newspaper articles published from 2016 to 2020 were analyzed. Through topic modeling of these newspaper articles over the past 5 years, 18 topics were derived and divided into four categories. Focusing on the derived topics and keywords, the topics derived in specific years and the proportion of topics by year were analyzed. The results of this study make it possible to grasp the needs of society projected through the press for doctors and medicine. Due to the nature of the press, topics that frequently appeared in newspaper articles were mainly social phenomena related to requirements for doctors, particularly dealing with economic and legal aspects. In particular, it was confirmed that doctors are now required to have a wider range of competencies that go beyond their required medical knowledge and clinical skills. This study helped to establish doctor's competencies by analyzing social needs for doctors through the latest research methods, and the findings could help to establish and improve doctor's competencies through ongoing research in the future.

• ETRI Journal
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• v.34 no.6
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• pp.966-969
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• 2012

From a practical viewpoint, the topic of electrical stability in oxide thin-film transistors (TFTs) has attracted strong interest from researchers. Positive bias stress and constant current stress tests on indium-gallium-zinc-oxide (IGZO)-TFTs have revealed that an IGZO-TFT with a larger Ga portion has stronger stability, which is closely related with the strong binding of O atoms, as determined from an X-ray photoelectron spectroscopy analysis.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.4
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• pp.318-329
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• 2022

This study analyzed keyword networks and Author's Affiliation networks of hydrogen-related papers published in Korea Citation Index (KCI) journals from 2016 to 2020. The study investigated co-occurrence patterns of institutions over time to examine collaboration trends of hydrogen scholars. The study also conducted frequency analysis of keyword networks to identify key topics and visualized keyword networks to explore topic trends. The result showed Collaborative research between institutions has not yet been extensively expanded. However, collaboration trends were much more pronounced with local universities. Keyword network analysis exhibited continuing diversification of topics in hydrogen research of Korea. In addition centrality analysis found hydrogen research mostly deals with multi-disciplinary and complex aspects like hydrogen production, transportation, and public policy.

• Journal of Korean Elementary Science Education
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• v.36 no.4
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• pp.379-393
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• 2017

This study analyzed the relationship between self-directedness of science-gifted elementary students and their expression of scientific creativity in science-gifted class. A science-gifted program on the topic of Hydraulic Machine was implemented to 34 fifth-graders in the Science-Gifted Education Center of an education office in Seoul, Korea for four weeks. The self-directedness of the gifted students was divided into three types of 'General, Scientific, and Topic-Related Self-Directedness'. The products of the students' activities were assessed by using a scientific creativity assessment formula. Qualitative research, such as analysis of observations and interviews, was also conducted in order to identify characteristics that were not apparently revealed by quantitative data. The main results of this study are as follows: First, science-gifted elementary students' general self-directedness and their scientific creativity were significantly correlated (r=.373). Second, the students' scientific self-directedness and scientific creativity did not have a significant correlation (r=.294). Third, there was a positive correlation between the students' topic-related self-directedness and their expression of scientific creativity. Their self-rated scores (r=.420) for the topic-related self-directedness and the number of activity types associated with the topic had a positive correlation (r=.350). Fourth, the students were categorized into four groups according to the levels of their self-directedness and scientific creativity, and the result showed that Type HH (high self-directedness and high scientific creativity) was the most common type (15 students, 38.5%), followed by Type LL (low self-directedness and low scientific creativity) (11 students, 28.2%). Eight (20.5%) and five students (12.8 %) belonged to Type LH (low self-directedness and high scientific creativity) and Type HL (high self-directedness and low scientific creativity) respectively. Fifth, the classroom observation of the students in groups revealed that groups with more number of Type HH demonstrated better cooperation and performance. Sixth, the analysis results of the observation were almost matched to the results of the self-directedness and scientific creativity tests. The students with higher self-directedness demonstrated active class participation and good cooperative skills. The students with higher scientific creativity had a tendency to generate creative ideas more frequently in given situations. Seventh, dynamic activities were perceived as enjoyable and exciting by 76.9% of the students, but static activities that require creativity were regarded as interesting only by 23.1% of the students. Among the students who were satisfied with both the creative and static activities, Type HH accounted for the largest proportion (55.6%). In conclusion, factors such as students' interests, initiatives, and attitudes displayed through voluntary participations originated from their own daily life can predict the degree of scientific creativity associated with the topic. Also, when students were categorized into four types according to the level of self-directedness and scientific creativity, there was a tendency of active behavior in class, cooperative skill, and activity satisfaction. This suggested that we should consider self-directedness and scientific creativity in selecting the gifted, grouping them in class, and designing and executing programs for science-gifted elementary students.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.74-75
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• 2017

In this paper, we analyzed 3,668 papers of JKIICE from 2007 to 2016 using text mining methods for understanding research fields. We used web scraping programs of Python language for data collection, and utilized topic modeling methods based on LDA algorithm implemented by R language. In the results, we verified that representative research areas of JKIICE could be downsized to 9 areas only by the analysis though the submission areas were 19 areas by 2016.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1156-1170
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• 2019

In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.45-61
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• 2019

Blockchain technology, which enables transparent transactions among individuals without central control, opens up diverse business possibilities. It is also expected that blockchain will have a ripple effect on the entire area of society including finance, manufacturing, distribution, and the public sector. Previous studies related to the blockchain also deals with its functional features and application to industrial and public fields. In the new technology such as blockchain, it is necessary to know what social perception is in order to create technological development environment, but there is a lack of research on it. Therefore, this study aims to find out the implications for industrial and policy direction by analyzing issues related to the blockchain in South Korea and the US through text mining. From these two countries, we collected text data related to blockchain in online communities and internet articles. Then, we did co-occurrence analysis and topic modeling on them respectively. As a result of this study, we have found common points and differences in keywords and topics extracted from social media in the two countries. Based on them, we can offer helpful suggestions for building a sound blockchain ecosystem, and directions for future research.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.445-456
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• 2024

This study aims to suggest service improvement opportunities by analyzing user review data of the top three OTT service apps(Netflix, Coupang Play, and TVING) on Google Play Store. To achieve this objective, we proposed a framework for uncovering service opportunities through the analysis of negative user reviews from OTT service providers. The framework involves automating the labeling of identified topics and generating service improvement opportunities using topic modeling and prompt engineering, leveraging GPT-4, a generative AI model. Consequently, we pinpointed five dissatisfaction topics for Netflix and TVING, and nine for Coupang Play. Common issues include "video playback errors", "app installation and update errors", "subscription and payment" problems, and concerns regarding "content quality". The commonly identified service enhancement opportunities include "enhancing and diversifying content quality". "optimizing video quality and data usage", "ensuring compatibility with external devices", and "streamlining payment and cancellation processes". In contrast to prior research, this study introduces a novel research framework leveraging generative AI to label topics and propose improvement strategies based on the derived topics. This is noteworthy as it identifies actionable service opportunities aimed at enhancing service competitiveness and satisfaction, instead of merely outlining topics.