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

Author

Listed:
  • Carol Shofiya

    (Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 1W5, Canada)

  • Samina Abidi

    (Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 1W5, Canada)

Abstract

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.

Suggested Citation

  • Carol Shofiya & Samina Abidi, 2021. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data," IJERPH, MDPI, vol. 18(11), pages 1-10, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5993-:d:567999
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    References listed on IDEAS

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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
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    Cited by:

    1. Gianpaolo Zammarchi & Francesco Mola & Claudio Conversano, 2023. "Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1001-1022, September.
    2. Bharati Sanjay Ainapure & Reshma Nitin Pise & Prathiba Reddy & Bhargav Appasani & Avireni Srinivasulu & Mohammad S. Khan & Nicu Bizon, 2023. "Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    3. Fernando Arias & Ariel Guerra-Adames & Maytee Zambrano & Efraín Quintero-Guerra & Nathalia Tejedor-Flores, 2022. "Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case," IJERPH, MDPI, vol. 19(16), pages 1-19, August.

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