• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.141-157
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• 2021

In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.43-50
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• 2021

This paper aims to identify trends of domestic industry-academic cooperation through comparative analysis of domestic and overseas academic articles published over the past 10 years (2011-2021). To this end, keyword network analysis and topic modeling analysis were performed to identify the characteristics of the entire articles collected. As results, it turned out that domestic articles included school, employment, education, patent, and professor as a major keyword while for overseas articles, project, policy, innovation, and company were the main topics, and related keywords were found to be influential. These results suggest that domestic industry-academic cooperation would have been designed and led by universities focusing on education for employment, and need to be carried out more actively in the areas of 'research' and 'technology transfer with the government's related policies and support on establishing two-way relationships that can benefit both schools and participating companies.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.1-28
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• 2021

FinTech has been discussed as an important business area towards technology-driven financial innovation. The term fintech is a combination of finance and technology, which means ICT technology currently associated with all finance areas. The popularity of the fintech industry has significantly increased over time, with full investment and support for numerous startups. Therefore, both academia and practice tried to analyze the trend of the fintech area. Despite the fact, however, previous research has limitations in terms of collecting relevant databases for fintech and identifying proper application areas. In response, this study proposed a new method for analyzing the trend of Fintech fields by expanding Fintech's terminology and using network analysis and topic modeling. A new Fintech terminology list was created and a total of 18,341 patents were collected from USPTO for 10 years. The co-classification analysis and network analysis was conducted to identify the technological trends of patent classification. In addition, topic modeling was conducted to identify the trends of fintech in order to analyze the contents of fintech. This study is expected to help both managers and investors who want to be involved in technology-driven financial services seize new FinTech technology opportunities.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.1-14
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• 2022

This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.57-79
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• 2022

COVID-19, which started in December 2019, has had a great impact on our lives in general, including politics, economy, society, and culture, and activities in sports and arts have also been significantly reduced. In the case of sports, sports for all fields in which ordinary citizens participate were particularly affected, and cases of infection in places closely related to people's lives, such as gyms, table tennis, and badminton clubs, also amplified the social fear of the spread of COVID-19. Therefore, in this study, we analyzed news articles related to sports for all at the time when COVID-19 was first spread, and investigated what issues were emerging and being discussed in the sports for all field under the COVID-19 situation. Specifically, we collected news articles dealt with sports for all issues under the COVID-19 situation from Korea's leading portal news sites and identified key sports for all issues by performing topic modeling on these articles. Through the analysis, we found meaningful issues such as COVID-19 outbreak in sports facilities and support for sports activities. In addition, through wordcloud analysis of these major issues, we visually understood the issues and identified the changes in these issues over time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.6
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• pp.496-503
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• 2008

With the improvement of computer power and technology, fracture modelling by finite element methods has become a topic of extensive studies. However, fracture simulation much limited to an academic study of crack propagation with a fine mesh. Element deletion method is a useful tool for estimating damage due to accidental or extreme loads on structures, provided that an effective and realistic criterion is established for simulating the material failure and subsequent element deletion. In this study, ABAQUS/Explicit is used to simulate the material failure on the basis of experimental results by X. Huang et al. Through numerical experiments, we suggest a formulation to determine the failure strain associated with the size and thickness of removed elements.

• Trans-
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• v.14
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• pp.65-92
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• 2023

The knowledge of the narrative agent not only constitutes the content and meaning of the narrative itself, but is also closely related to the emotional response of the recipient. Also, the disparity of knowledge between narrative agents is an important factor in making a narrative richer and more interesting. But It tends to be treated as a sub-topic of narration theory or genre/style studies rather than an independent subject of narrative studies or criticism. In this paper, I propose a model that can systematically and quantitatively analyze the knowledge of narrative agents. The proposed model consists of the knowledge structure that represents a narrative, the knowledge state that expresses the knowledge of narrative agent as a degree of belief, and the knowledge flow that means changes in the knowledge state according to the development of events. In addition, the formal notation of the knowledge structure and a probabilistic inference model that could obtain the state of knowledge were proposed, and the knowledge structure and knowledge flow were analyzed by applying the model to the actual narrative. It is expected that the proposed model will be of practical help in the creation and evaluation of narratives.

• Korea Trade Review
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• v.45 no.6
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• pp.23-43
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• 2020

This study aims at analyzing the interactions between two of the most significant trade stakeholders in Korea, the Trade Ministry and the Legislature, using text network analysis. Tackling seven Action and Plan Reports for Requests from Parliamentary Inspection released by the National Assembly, this paper conducts a topic modelling analysis, particularly focusing on the reports for the three trade-related institutes: the MOTIE headquarter, Korea Trade Insurance Corporation, Korea Trade and Investment Promotion Agency. According to the analysis, such traditional topics of the MOTIE as enterprise, industry, business, management, development were frequently appeared in the reports. Trade-related topics including export, trade, commerce, investment, overseas, domestic, dispute, cooperation, efficiency, negotiation, service, promotion were repeatedly shown. Lastly, a case study on 2019 Parliamentary Inspection Report showed specific trade-related topics and relevant contents that raised issues in that year. This analysis implies that the text data driven from the Parliamentary Inspection Reports between the MOTIE and the National Assembly, can be established as so called 'trade policy information system' which are valuable not only for the two but also the rest of the trade stakeholders in Korea.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.541-572
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• 2023

Amazon Go is a cashierless convenience store concept, which is seen as a disruption in the grocery retail segment. Although Amazon Go has the ability to disrupt the retail segment, there are speculations on how Amazon Go will be perceived by users. Existing studies have not utilized user-generated content to understand the factors that affect customer behaviour in case of Amazon Go. Additionally, in case of phygital retail, studies have not attempted at understanding the effect of emotions and gratifications on user behaviour. To address the gap of exploring user perspectives based on their experience, we have examined the impact of gratifications and emotions on the acceptance of phygital retail using user-generated-content. A mixed-method approach has been utilized using only user-generated content. Utilizing topic-modelling based content analysis and emotion analysis on 30 articles related to Amazon Go, we found themes like, convenience, technology, experience, personalization, enjoyment and emotions like, bad, good, annoyance, success. In the empirical analysis, we have utilized 522 reviews about Amazon Go from the cognition and emotion theory stance, and found that hedonic gratifications have a positive impact on challenge emotions. We also found a significant impact of emotions on customer's favourite behaviour.