IDEAS home Printed from https://ideas.repec.org/a/igg/jswis0/v19y2023i1p1-18.html
   My bibliography  Save this article

Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination

Author

Listed:
  • Doğan Küçük

    (Department of Computer Engineering, Graduate School of Natural and Applied Sciences, Gazi University, Turkey)

  • Nursal Arıcı

    (Department of Management Information Systems, Faculty of Applied Sciences, Gazi University, Turkey)

Abstract

Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.

Suggested Citation

  • Doğan Küçük & Nursal Arıcı, 2023. "Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-18, January.
    • Handle: RePEc:igg:jswis0:v:19:y:2023:i:1:p:1-18
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.333865
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Titilayo Adebola, 2022. "Geographical indications in the era of the African Continental Free Trade Area (AfCFTA)," Journal of Intellectual Property Law and Practice, Oxford University Press, vol. 17(9), pages 748-760.
    2. Yuhu Zhao & Xiangping Mei & Jianqiang Guo, 2023. "Influence of Sustainable Environment Based on a SWOT-PEST Model on Sports Tourism Service Integration Development," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    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.

      More about this item

      Statistics

      Access and download statistics

      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:igg:jswis0:v:19:y:2023:i:1:p:1-18. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.