AUTHOR=Huang Shih-Hsio , Tsao Shu-Feng , Chen Helen , Bin Noon Gaya , Li Lianghua , Yang Yang , Butt Zahid Ahmad 

TITLE=Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada

JOURNAL=International Journal of Public Health

VOLUME=67

YEAR=2022

URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2022.1605241

DOI=10.3389/ijph.2022.1605241

ISSN=1661-8564

ABSTRACT=<p><bold>Objectives:</bold> This study aimed to investigate public discourses and sentiments regarding the Freedom Convoy in Canada on Twitter.</p><p><bold>Methods:</bold> English tweets were retrieved from Twitter API from 15 January to 14 February 2022 when the Freedom Convoy occurred. Unsupervised topic modelling and sentiment analysis were applied to identify topics and sentiments for each topic.</p><p><bold>Results:</bold> Five topics resulted from the topic modelling, including convoy support, political arguments toward the current prime minister, lifting vaccine mandates, police activities, and convoy fundraising. Overall, sentiments for each topic began with more positive or negative sentiments but approached to neutral over time.</p><p><bold>Conclusion:</bold> The results show that sentiments towards the Freedom Convoy generally tended to be positive. Five topics were identified from the data collected, and these topics highly correlated with the events of the convoy. Our study also demonstrated that a mixed approach of unsupervised machine learning techniques and manual validation could generate timely evidence.</p>