Note: Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter
Note: Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter doi: 10.1371/journal.pone.0239441 Aim: Twitter uses’ discourse + physiological reactions to COVID19 (output based on word) Method Using machine learning to analyse 1.9 million Tweets (written in English) - related to Coronavirus Collection 23 Jan 2020 - 7 Mar 2020 11 salient topics แยกออกมาเป็น 10 กลุ่ม “updates about confirmed cases,” “COVID-19 related death,” “cases outside China (worldwide),” “COVID-19 outbreak in South Korea,” “early signs of the outbreak in New York,” “Diamond Princess cruise,” “economic impact,” “Preventive measures,” “Authorities,” “supply chain Results Not reveal treatments and symptoms related messages Sentiment analysis fear for nature of coronavirus implications and limitation -- discussed in maintext Machine learning approach; (no code deposit) Latent topics relates to COVID19 identified ...