IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p5993-d567999.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/5993/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/11/5993/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    2. Müller-Hansen, Finn & Lee, Yuan Ting & Callaghan, Max & Jankin, Slava & Minx, Jan C., 2022. "The German coal debate on Twitter: Reactions to a corporate policy process," Energy Policy, Elsevier, vol. 169(C).
    3. Lipizzi, Carlo & Iandoli, Luca & Ramirez Marquez, José Emmanuel, 2015. "Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams," International Journal of Information Management, Elsevier, vol. 35(4), pages 490-503.
    4. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    5. Daesik Kim & Chung Joo Chung & Kihong Eom, 2022. "Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    6. David M. Goldberg & Nohel Zaman & Arin Brahma & Mariano Aloiso, 2022. "Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(3), pages 419-437, March.
    7. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    8. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    9. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    10. Cohen, Scott & Stienmetz, Jason & Hanna, Paul & Humbracht, Michael & Hopkins, Debbie, 2020. "Shadowcasting tourism knowledge through media: Self-driving sex cars?," Annals of Tourism Research, Elsevier, vol. 85(C).
    11. Zhang, Xuetong & Zhang, Weiguo, 2023. "Information asymmetry, sentiment interactions, and asset price," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    12. Takahiro Yabe & P. Suresh C. Rao & Satish V. Ukkusuri, 2021. "Modeling the Influence of Online Social Media Information on Post-Disaster Mobility Decisions," Sustainability, MDPI, vol. 13(9), pages 1-13, May.
    13. Patricia P. Iglesias-Sánchez & Gustavo Fabián Vaccaro Witt & Francisco E. Cabrera & Carmen Jambrino-Maldonado, 2020. "The Contagion of Sentiments during the COVID-19 Pandemic Crisis: The Case of Isolation in Spain," IJERPH, MDPI, vol. 17(16), pages 1-10, August.
    14. Indy Wijngaards & Martijn Burger & Job van Exel, 2019. "The promise of open survey questions—The validation of text-based job satisfaction measures," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-22, December.
    15. Junegak Joung & Ki-Hun Kim & Kwangsoo Kim, 2021. "Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective," SAGE Open, , vol. 11(1), pages 21582440209, January.
    16. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).
    17. Frantisek Darena & Jonas Petrovsky & Jan Zizka & Jan Prichystal, 2016. "Analyzing the correlation between online texts and stock price movements at micro-level using machine learning," MENDELU Working Papers in Business and Economics 2016-67, Mendel University in Brno, Faculty of Business and Economics.
    18. Xu, Xun & Lee, Chieh, 2020. "Utilizing the platform economy effect through EWOM: Does the platform matter?," International Journal of Production Economics, Elsevier, vol. 227(C).
    19. Ema Kušen & Mark Strembeck, 2021. "“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters," Journal of Computational Social Science, Springer, vol. 4(2), pages 531-565, November.
    20. Wen Zhang & Daniel R. Fesenmaier, 2018. "Assessing emotions in online stories: comparing self-report and text-based approaches," Information Technology & Tourism, Springer, vol. 20(1), pages 83-95, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5993-:d:567999. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.