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

Objectives: This study aimed to investigate public discourses and sentiments regarding the Freedom Convoy in Canada on Twitter. Methods: English tweets were retrieved from Twitter API from 15 January to 14 February 2022 when the Freedom Convoy occurred. Unsupervised topic modelling and sentiment ana...

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Autores principales: Huang, Shih-Hsio, Tsao, Shu-Feng, Chen, Helen, Bin Noon, Gaya, Li, Lianghua, Yang, Yang, Butt, Zahid Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9649435/
https://www.ncbi.nlm.nih.gov/pubmed/36387289
http://dx.doi.org/10.3389/ijph.2022.1605241
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author Huang, Shih-Hsio
Tsao, Shu-Feng
Chen, Helen
Bin Noon, Gaya
Li, Lianghua
Yang, Yang
Butt, Zahid Ahmad
author_facet Huang, Shih-Hsio
Tsao, Shu-Feng
Chen, Helen
Bin Noon, Gaya
Li, Lianghua
Yang, Yang
Butt, Zahid Ahmad
author_sort Huang, Shih-Hsio
collection PubMed
description Objectives: This study aimed to investigate public discourses and sentiments regarding the Freedom Convoy in Canada on Twitter. Methods: 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. Results: 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. Conclusion: 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.
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spelling pubmed-96494352022-11-15 Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada Huang, Shih-Hsio Tsao, Shu-Feng Chen, Helen Bin Noon, Gaya Li, Lianghua Yang, Yang Butt, Zahid Ahmad Int J Public Health Public Health Archive Objectives: This study aimed to investigate public discourses and sentiments regarding the Freedom Convoy in Canada on Twitter. Methods: 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. Results: 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. Conclusion: 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. Frontiers Media S.A. 2022-10-28 /pmc/articles/PMC9649435/ /pubmed/36387289 http://dx.doi.org/10.3389/ijph.2022.1605241 Text en Copyright © 2022 Huang, Tsao, Chen, Bin Noon, Li, Yang and Butt. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health Archive
Huang, Shih-Hsio
Tsao, Shu-Feng
Chen, Helen
Bin Noon, Gaya
Li, Lianghua
Yang, Yang
Butt, Zahid Ahmad
Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada
title Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada
title_full Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada
title_fullStr Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada
title_full_unstemmed Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada
title_short Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada
title_sort topic modelling and sentiment analysis of tweets related to freedom convoy 2022 in canada
topic Public Health Archive
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9649435/
https://www.ncbi.nlm.nih.gov/pubmed/36387289
http://dx.doi.org/10.3389/ijph.2022.1605241
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