Table 1.

Examples of positive, neutral, and negative tweets with VADER^a-assigned sentiment scores.

Sentiment score	Classification	Tweet
0.93	Positive	“Thank you so much @johnkrasinski for this series! I think it helped remind everyone how much good there is in the world. I really hope the silver lining of COVID-19 is people continue to be kinder to one another and truly realize we're all in this together.”
0.65	Positive	“@celliottability notes that Ontario has made great strides on COVID-19 testing and contact tracing. Anyone who wants to get a COVID-19 test can do so, even if they don’t have symptoms”
0.03	Neutral	“#SSHRCResearchers Helen Kennedy and Sarah Atkinson look at how the industry is adapting to the new reality of #COVID19”
–0.04	Neutral	“Why you should wear a #mask #COVID10 @ottawahealth”
–0.40	Negative	“COVID-19 Compliance: One-in-five Canadians making little to no effort to stop coronavirus spread”
–0.57	Negative	“Because the Chinese just hate witchcraft. Riiiiight... Cough, feng shui, cough #WuhanVirus #COVID19”

^aVADER: Valence Aware Dictionary and Sentiment Reasoner.