IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2020i1p218-d470511.html
   My bibliography  Save this article

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Author

Listed:
  • Sumayh S. Aljameel

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Dina A. Alabbad

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Norah A. Alzahrani

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Shouq M. Alqarni

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Fatimah A. Alamoudi

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Lana M. Babili

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Somiah K. Aljaafary

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Fatima M. Alshamrani

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

Abstract

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

Suggested Citation

  • Sumayh S. Aljameel & Dina A. Alabbad & Norah A. Alzahrani & Shouq M. Alqarni & Fatimah A. Alamoudi & Lana M. Babili & Somiah K. Aljaafary & Fatima M. Alshamrani, 2020. "A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia," IJERPH, MDPI, vol. 18(1), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:218-:d:470511
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/1/218/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/1/218/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carlos de las Heras-Pedrosa & Pablo Sánchez-Núñez & José Ignacio Peláez, 2020. "Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems," IJERPH, MDPI, vol. 17(15), pages 1-22, July.
    2. Nourah F. Bin Hathlian & Alaaeldin M. Hafez, 2017. "Subjective Text Mining for Arabic Social Media," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(2), pages 1-13, April.
    3. Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    4. Mohammed Rushdi-Saleh & M. Teresa Martín-Valdivia & L. Alfonso Ureña-López & José M. Perea-Ortega, 2011. "OCA: Opinion corpus for Arabic," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 2045-2054, October.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    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. Fernando Olivares-Delgado & Patricia P. Iglesias-Sánchez & María Teresa Benlloch-Osuna & Carlos de las Heras-Pedrosa & Carmen Jambrino-Maldonado, 2020. "Resilience and Anti-Stress during COVID-19 Isolation in Spain: An Analysis through Audiovisual Spots," IJERPH, MDPI, vol. 17(23), pages 1-23, November.
    2. Andrea Castro-Martinez & Paula Méndez-Domínguez & Aimiris Sosa Valcarcel & Joaquín Castillo de Mesa, 2021. "Social Connectivity, Sentiment and Participation on Twitter during COVID-19," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    3. Carlos de las Heras-Pedrosa & Carmen Jambrino-Maldonado & Dolores Rando-Cueto & Patricia P. Iglesias-Sánchez, 2022. "COVID-19 Study on Scientific Articles in Health Communication: A Science Mapping Analysis in Web of Science," IJERPH, MDPI, vol. 19(3), pages 1-29, February.
    4. Zvjezdana Gvozdanović & Nikolina Farčić & Hrvoje Šimić & Vikica Buljanović & Lea Gvozdanović & Sven Katalinić & Stana Pačarić & Domagoj Gvozdanović & Željka Dujmić & Blaženka Miškić & Ivana Barać & Na, 2021. "The Impact of Education, COVID-19 and Risk Factors on the Quality of Life in Patients with Type 2 Diabetes," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    5. María Jesús Fernández-Torres & Ana Almansa-Martínez & Rocío Chamizo-Sánchez, 2021. "Infodemic and Fake News in Spain during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(4), pages 1-13, February.
    6. Karol Król & Dariusz Zdonek, 2023. "Cultural Heritage Topics in Online Queries: A Comparison between English- and Polish-Speaking Internet Users," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    7. Antonio Millán-Jiménez & Rafael Herrera-Limones & Álvaro López-Escamilla & Emma López-Rubio & Miguel Torres-García, 2021. "Confinement, Comfort and Health: Analysis of the Real Influence of Lockdown on University Students during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(11), pages 1-15, May.
    8. Lin, Trisha T. C., 2022. "Investigating the relationship of disguised socialbots and disinformation threat in Taiwan," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265654, International Telecommunications Society (ITS).
    9. Xosé López-García & Carmen Costa-Sánchez & Ángel Vizoso, 2021. "Journalistic Fact-Checking of Information in Pandemic: Stakeholders, Hoaxes, and Strategies to Fight Disinformation during the COVID-19 Crisis in Spain," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    10. Suzanne Elayan & Martin Sykora, 2024. "Digital intermediaries in pandemic times: social media and the role of bots in communicating emotions and stress about Coronavirus," Journal of Computational Social Science, Springer, vol. 7(3), pages 2481-2504, December.
    11. Ebaa Fayyoumi & Sahar Idwan, 2021. "Semantic Partitioning and Machine Learning in Sentiment Analysis," Data, MDPI, vol. 6(6), pages 1-17, June.
    12. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    13. Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.
    14. Hyehyun Hong & Hyo Jung Kim, 2020. "Antecedents and Consequences of Information Overload in the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    15. Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    16. Melissa MacKay & Taylor Colangeli & Daniel Gillis & Jennifer McWhirter & Andrew Papadopoulos, 2021. "Examining Social Media Crisis Communication during Early COVID-19 from Public Health and News Media for Quality, Content, and Corresponding Public Sentiment," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    17. Domingo Palacios-Ceña & César Fernández-de-las-Peñas & Lidiane L. Florencio & Ana I. de-la-Llave-Rincón & María Palacios-Ceña, 2020. "Emotional Experience and Feelings during First COVID-19 Outbreak Perceived by Physical Therapists: A Qualitative Study in Madrid, Spain," IJERPH, MDPI, vol. 18(1), pages 1-17, December.
    18. Ziqiang Han & Mengfan Shen & Hongbing Liu & Yifan Peng, 2022. "Topical and emotional expressions regarding extreme weather disasters on social media: a comparison of posts from official media and the public," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    19. Abdelghani Ghanem & Chaimae Asaad & Hakim Hafidi & Youness Moukafih & Bassma Guermah & Nada Sbihi & Mehdi Zakroum & Mounir Ghogho & Meriem Dairi & Mariam Cherqaoui & Karim Baina, 2021. "Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management," IJERPH, MDPI, vol. 18(22), pages 1-19, November.
    20. Qi Wu & Beian Chen & Jianping Zhu, 2023. "Insights from COVID-19: Reflecting on the Promotion of Long-Term Health Policies in China," IJERPH, MDPI, vol. 20(4), pages 1-17, February.

    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:2020:i:1:p:218-:d:470511. 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.