![]() Therefore, social media data, such as tweets, have become even more important in health research associated with the current pandemic. During this global crisis, when people have been forced to stay at home and connect virtually, social media platforms have played an increasingly significant role in communications now more than ever before. In Canada, it has led to over 1 million positive cases and caused more than 24,000 deaths. The Coronavirus disease (COVID-19) pandemic has persisted for more than a year and resulted in over 141 million infections with over 3 million deaths worldwide. Our research has also demonstrated a social listening approach to identify what the public sentiments and opinions are in a timely manner. The average sentiment compound score for each topic appeared to be slightly positive, yet the daily sentiment compound scores varied greatly between positive and negative emotions for each topic.Ĭonclusion: Our study results have shown a slightly positive sentiment on average during the second wave of the COVID-19 pandemic in Ontario, along with six topics. Results: Vaccine, pandemic, business, lockdown, mask, and Ontario were six topics identified from the unsupervised topic modelling. VADER was used to calculate daily and average sentiment compound scores for topics identified. Latent Dirichlet Allocation was used for unsupervised topic modelling. The daily number of COVID-19 cases was retrieved from the Ontario provincial government’s public health database. Dates of vaccine-related events and policy changes were collected from public health units in Ontario. ![]() Methods: Tweets were collected from December 5, 2020, to March 6, 2021, excluding non-individual accounts. Objective: This study aimed to explore topics and sentiments using tweets from Ontario, Canada, during the second wave of the COVID-19 pandemic. ![]()
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