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Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data

Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social...

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Autores principales: Shofiya, Carol, Abidi, Samina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199732/
https://www.ncbi.nlm.nih.gov/pubmed/34204907
http://dx.doi.org/10.3390/ijerph18115993
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author Shofiya, Carol
Abidi, Samina
author_facet Shofiya, Carol
Abidi, Samina
author_sort Shofiya, Carol
collection PubMed
description Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textual data. Methods: Twitter data specific to Canada and texts comprising social distancing keywords were extrapolated, followed by utilizing the SentiStrength tool to extricate sentiment polarity of tweet texts. Thereafter, the support vector machine (SVM) algorithm was employed for sentiment classification. Evaluation of performance was measured with a confusion matrix, precision, recall, and F1 measure. Results: This study resulted in the extraction of a total of 629 tweet texts, of which, 40% of tweets exhibited neutral sentiments, followed by 35% of tweets showed negative sentiments and only 25% of tweets expressed positive sentiments towards social distancing. The SVM algorithm was applied by dissecting the dataset into 80% training and 20% testing data. Performance evaluation resulted in an accuracy of 71%. Upon using tweet texts with only positive and negative sentiment polarity, the accuracy increased to 81%. It was observed that reducing test data by 10% increased the accuracy to 87%. Conclusion: Results showed that an increase in training data increased the performance of the algorithm.
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spelling pubmed-81997322021-06-14 Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data Shofiya, Carol Abidi, Samina Int J Environ Res Public Health Article Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textual data. Methods: Twitter data specific to Canada and texts comprising social distancing keywords were extrapolated, followed by utilizing the SentiStrength tool to extricate sentiment polarity of tweet texts. Thereafter, the support vector machine (SVM) algorithm was employed for sentiment classification. Evaluation of performance was measured with a confusion matrix, precision, recall, and F1 measure. Results: This study resulted in the extraction of a total of 629 tweet texts, of which, 40% of tweets exhibited neutral sentiments, followed by 35% of tweets showed negative sentiments and only 25% of tweets expressed positive sentiments towards social distancing. The SVM algorithm was applied by dissecting the dataset into 80% training and 20% testing data. Performance evaluation resulted in an accuracy of 71%. Upon using tweet texts with only positive and negative sentiment polarity, the accuracy increased to 81%. It was observed that reducing test data by 10% increased the accuracy to 87%. Conclusion: Results showed that an increase in training data increased the performance of the algorithm. MDPI 2021-06-03 /pmc/articles/PMC8199732/ /pubmed/34204907 http://dx.doi.org/10.3390/ijerph18115993 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shofiya, Carol
Abidi, Samina
Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
title Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
title_full Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
title_fullStr Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
title_full_unstemmed Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
title_short Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data
title_sort sentiment analysis on covid-19-related social distancing in canada using twitter data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199732/
https://www.ncbi.nlm.nih.gov/pubmed/34204907
http://dx.doi.org/10.3390/ijerph18115993
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