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Note: Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study doi: http://dx.doi.org/10.2196/21978 Gap; Lack of infoveillence (information watchfulness) on covid19 Objective; Increase public awareness of COVID-19 pandemic trends Uncover  meaningful themes of concern which shown by twitter (using english language to search) Method; Data mining Total 107,990 tweets related to COVID December 13 - Mar 9 2020 -- 3 months Frequency of keywords,  Sentiment analysis, and  Topic modeling to identify and Explore discussion topics over time Natural language processing + Dirichlet allocation algorithm Identify most common tweet topics Categorized clusters Identify themes based on keyword analysis Result; Indicate three main aspects of public awareness+concern Trend of spread + symptoms of covid -- 3 stages Sentimental analysis reveals negative Topic modeling -- themes divide into 3 Covid pandemic emergency How to control covid Report on co...

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 ...