IDEAS home Printed from https://ideas.repec.org/a/arb/journl/y2023i2p6-23.html
   My bibliography  Save this article

Applying Bert and Vader in HR Sentiment Analysis

Author

Listed:
  • Marian Pompiliu Cristescu

    (Lucian Blaga University of Sibiu, Romania)

  • Dumitru Alexandru Mara

    (Lucian Blaga University of Sibiu, Romania)

  • Lia-Cornelia Culda

    (Lucian Blaga University of Sibiu, Romania)

  • Raluca Andreea NeriÈ™anu

    (Lucian Blaga University of Sibiu, Romania)

Abstract

In today's business world, it's more important than ever to understand how employees feel. This study delves into the efficacy of open-source tools for sentiment analysis of employee feedback. We devised a Python-based solution, employing BERT and VADER libraries to process and analyze textual data. Our research reveals significant disparities in the sentiment polarity results obtained from these tools, with BERT showing a positivity bias and VADER offering a more balanced sentiment distribution. These findings highlight the importance of selecting appropriate tools for sentiment analysis tasks, tailored to the specific nature of the text. Overall, our study demonstrates the viability and importance of open-source tools in sentiment analysis, particularly in enhancing continuous feedback mechanisms within organizational contexts.

Suggested Citation

  • Marian Pompiliu Cristescu & Dumitru Alexandru Mara & Lia-Cornelia Culda & Raluca Andreea NeriÈ™anu, 2023. "Applying Bert and Vader in HR Sentiment Analysis," HR and Technologies, Creative Space Association, issue 2, pages 6-23.
    • Handle: RePEc:arb:journl:y:2023:i:2:p:6-23
    as

    Download full text from publisher

    File URL: https://journal.cspace-ngo.com/arb/Issues/2023/2/1-Marian-Cristescu-et-al-2023-2.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    HR analytics; Python; sentiment analysis; BERT; VADER;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:arb:journl:y:2023:i:2:p:6-23. 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: Rositsa Zarkova (email available below). General contact details of provider: .

    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.