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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9649435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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