IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v77y2025ipas027553192500131x.html
   My bibliography  Save this article

Access to credit and fintech: A lexicon-based sentiment analysis application on Twitter data

Author

Listed:
  • Bredice, Marilena
  • Formisano, Anna Vittoria
  • Kullafi, Sara
  • Palma, Pasquale

Abstract

This study examines how the interaction between access to credit and new fintech ventures evolved during and after the COVID-19 pandemic by conducting three different lexicon-based sentiment analyses using NLTK, TextBlob, and Flair Python libraries. We previously gathered data from Twitter (subsequently rebranded as X) by applying different combinations of keywords in our scraper script to better understand the phenomenon and enhance the quality of the final dataset. We defined the most appropriate set of keywords that we subsequently used for analysis. We also empirically estimated whether the results obtained could be generalized to the continents involved. Although the keywords “access to credit” and “fintech” show a slight decrease in tweets at the end of the COVID-19 pandemic, we obtain meaningful insights at the continent level concerning variations in sentiment over the analyzed period. Furthermore, the most recurrent keywords show significant correlations.

Suggested Citation

  • Bredice, Marilena & Formisano, Anna Vittoria & Kullafi, Sara & Palma, Pasquale, 2025. "Access to credit and fintech: A lexicon-based sentiment analysis application on Twitter data," Research in International Business and Finance, Elsevier, vol. 77(PA).
    • Handle: RePEc:eee:riibaf:v:77:y:2025:i:pa:s027553192500131x
    DOI: 10.1016/j.ribaf.2025.102875
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S027553192500131X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2025.102875?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Access to credit; Fintech; Sentiment analysis; Twitter; Lexicon-based approach;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

    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:eee:riibaf:v:77:y:2025:i:pa:s027553192500131x. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    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.