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Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture

Author

Listed:
  • Carlos Henríquez Miranda

    (Facultad de Ingeniería, Universidad del Magdalena; Santa Marta 470001, Colombia)

  • German Sanchez-Torres

    (Facultad de Ingeniería, Universidad del Magdalena; Santa Marta 470001, Colombia)

  • Dixon Salcedo

    (Department of Computer Science and Electronics, University of the Coast, Barranquilla 080020, Colombia)

Abstract

The COVID-19 pandemic has had a significant impact on various aspects of society, including economic, health, political, and work-related domains. The pandemic has also caused an emotional effect on individuals, reflected in their opinions and comments on social media platforms, such as Twitter. This study explores the evolution of sentiment in Spanish pandemic tweets through a data analysis based on a fine-tuned BERT architecture. A total of six million tweets were collected using web scraping techniques, and pre-processing was applied to filter and clean the data. The fine-tuned BERT architecture was utilized to perform sentiment analysis, which allowed for a deep-learning approach to sentiment classification. The analysis results were graphically represented based on search criteria, such as “COVID-19” and “coronavirus”. This study reveals sentiment trends, significant concerns, relationship with announced news, public reactions, and information dissemination, among other aspects. These findings provide insight into the emotional impact of the COVID-19 pandemic on individuals and the corresponding impact on social media platforms.

Suggested Citation

  • Carlos Henríquez Miranda & German Sanchez-Torres & Dixon Salcedo, 2023. "Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture," Data, MDPI, vol. 8(6), pages 1-18, May.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:6:p:96-:d:1158345
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    References listed on IDEAS

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    1. Harleen Kaur & Shafqat Ul Ahsaan & Bhavya Alankar & Victor Chang, 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets," Information Systems Frontiers, Springer, vol. 23(6), pages 1417-1429, December.
    2. Mario Jojoa & Begonya Garcia-Zapirain & Marino J. Gonzalez & Bernardo Perez-Villa & Elena Urizar & Sara Ponce & Maria Fernanda Tobar-Blandon, 2022. "Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques," IJERPH, MDPI, vol. 19(9), pages 1-20, May.
    3. Gianluca Bonifazi & Enrico Corradini & Domenico Ursino & Luca Virgili, 2022. "New Approaches to Extract Information From Posts on COVID-19 Published on Reddit," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1385-1431, September.
    4. Petra Kralj Novak & Jasmina Smailović & Borut Sluban & Igor Mozetič, 2015. "Sentiment of Emojis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    5. Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
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