• Communications of Mathematical Education
• /
• v.33 no.3
• /
• pp.275-294
• /
• 2019

In order to understand the recent trends in mathematics education research papers, data mining method was applied to analyze journals of the mathematics education posterior to the year of 2016. Text mining method is useful in the sense that it utilizes statistical approach to understand the linkages and influencing relationship between concepts and deriving the meaning that data shows by visualizing the process. Therefore, this research analyzed the key words largely mentioned in the recent mathematics education journals. Also the correlation between the subjects of mathematics education was deduced by using topic modeling. By using the trend analysis tool it is possible to understand the vital point which researchers consider it as important in recent mathematics education area and at the same time we tried to use it as a fundamental data to decide the upcoming research topic that is worth noticing.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.5
• /
• pp.731-740
• /
• 2021

This study applied LDA topic modeling analysis to collect and analyze news media big data related to face masks in the three waves of the COVID-19 pandemic in Korea. The results empirically show that media reports focused on mask production and distribution policies in the first wave and the mandatory mask wearing in the second wave. In contrast, more reports on trivial, gossipy events consist of the media coverage in the second and third waves. The findings imply that Korea's governmental interventions to address the shortage of face masks and to regulate mask wearing were successful relatively in a short time. In contrast, the study also reports that there may be relative less number of science-based news reports like the ones on the effectiveness of face masks or different levels of filter types. This study exemplifies how a big data analysis can be applied to evaluate and enhance public health communication.

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

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

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.238-240
• /
• 2004

현재의 웹은 사용자가 원하는 정보를 정확하고 빠르게 검색 결과를 보여주지 못하는 단점이 있다. 그러므로 사용자에게 정확한 정보 전달을 해 주고자 시맨틱 웹이 등장하게 되었다. 시맨틱 웹은 기계가 이해할 수 있는 온톨로지를 구성하여 사용자가 원하는 정보를 정확하게 전달해 줄 수 있다는 점에서 미래의 웹으로 각광을 받게 될 것이다. 시맨틱 웹의 기반이 되고 있는 온톨로지는 어떤 특정 도메인에서 사용되는 정보들과 그 정보들 간의 관계를 정의해 놓은 것으로 관련 도메인 전문가들과 협의에 의하여 개념들과 관계들의 구조를 정하고 이를 기반으로 구축된다. 실제의 응용 시스템에서는 도메인마다의 구체적인 지식을 포함하는 온톨로지 설계가 필요하다. 이 논문에서는 택배회사가 홈쇼핑사이트 업체를 대상으로 효율적인 마케팅을 하기 위친 홈쇼핑사이트에 대한 기본정보를 추출하는 것을 목적으로 한다. 온톨로지를 구축하는 온톨로지 언어에는 RDF, RDF(S), DAML＋OIL, OWL. Topic Map등이 있다. 이 논문에서는 토픽맵을 사용하여 홈쇼핑 사이트 정보를 검색하기 위한 홈쇼핑 사이트에 대한 온톨로지를 설계하였다.

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

• The Journal of Asian Finance, Economics and Business
• /
• v.4 no.3
• /
• pp.85-94
• /
• 2017

The paper examines critical factors affecting consumer behavioral intentions in accepting online health communication through social networking sites. Unlike recent research under this topic, the paper assimilates some components of service quality dimensions and consumer behavior theories. The paper employs factor analysis and structural equation modelling analysis with latent variables to identify critical factors from the survey data collected from Korean consumers. The results of the study identifies three major constructs: consumer needs for health information, the perceived value of tangible attributes of health information providers, and the perceived value of intangible attributes of health information providers. The results show that consumer needs for health information and the tangible and intangible attributes of health information providers should be considered as important antecedents of accepting online health communication through social networking sites. The findings suggest that the success of online health communication via social networking sites largely depends on the tangible and intangible attributes of health information providers.

• Trans-
• /
• v.14
• /
• pp.65-92
• /
• 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.

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

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