ABSTRACT
By 2019 COVID-19, since the epidemic, the number of relevant documents exponentially level rise. Faced with a large amount of literature, this research provides convenience for exploring the connection between research topics and fields and quickly understanding relevant literature information. We pass on the data set after data cleansing using the LDA(Latent Dirichlet allocation) methods, and Berts and K-means modeling method extracting topic keywords. Use knowledge graph tools to output relevant visual graphics and systematically extract adequate information. Through text mining of biomedical research papers related to COVID-19, the improved model is used to analyze and make recommendations to respond to and prevent the COVID-19 pandemic. This research can support the rapid and in-depth analysis of a large number of relevant documents and can be used in future research to support real-time scientific disease research.
ABSTRACT
Over the past two years, the Covid-19 pandemic has changed the way customers consume brands during the periodic lockdowns that happened in Thailand. Twenty young Thai adults were interviewed in a qualitative research about changes in their brand consumption as a direct result of having to stay at home for months to prevent the spread of Covid-19. The interviewees were asked open-ended questions about their favorite and most consumed brands before lockdowns and how the preferences changed during lockdowns as well as how the pandemic has changed their brand perceptions. The results showed a huge and relatively permanent increase in customers spending more time and money online with their preferences shifting to brands that have a strong digital presence. In order for companies to adapt to these changes, they must prioritize maximizing value through key factors which include smoothly transitioning to digitization, offering affordable products with constant promotions, and ensuring fast, reliable deliveries. © 2022 IEEE.