IDEAS home Printed from https://ideas.repec.org/a/igg/jsda00/v11y2022i2p1-16.html
   My bibliography  Save this article

BERT-BU12 Hate Speech Detection Using Bidirectional Encoder-Decoder

Author

Listed:
  • Shailja Gupta

    (Manav Rachna University, India)

  • Manpreet Kaur

    (Manav Rachna University, India)

  • Sachin Lakra

    (Manav Rachna University, India)

Abstract

In the recent times transfer learning models have known to exhibited good results in the area of text classification for question-answering, summarization, next word prediction but these learning models have not been extensively used for the problem of hate speech detection yet. We anticipate that these networks may give better results in another task of text classification i.e. hate speech detection. This paper introduces a novel method of hate speech detection based on the concept of attention networks using the BERT attention model. We have conducted exhaustive experiments and evaluation over publicly available datasets using various evaluation metrics (precision, recall and F1 score). We show that our model outperforms all the state-of-the-art methods by almost 4%. We have also discussed in detail the technical challenges faced during the implementation of the proposed model.

Suggested Citation

  • Shailja Gupta & Manpreet Kaur & Sachin Lakra, 2022. "BERT-BU12 Hate Speech Detection Using Bidirectional Encoder-Decoder," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(2), pages 1-16, August.
    • Handle: RePEc:igg:jsda00:v:11:y:2022:i:2:p:1-16
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSDA.20220701.oa4
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ravish Himmatlal Hirpara & Shambhu Nath Sharma, 2020. "An Analysis of a Wind Turbine-Generator System in the Presence of Stochasticity and Fokker-Planck Equations," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 9(1), pages 18-43, 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.
    1. Aniket Agarwal & Kirti Pal, 2021. "Optimization of Unit Commitment Problem Using Genetic Algorithm," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 21-37, July.

    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:jsda00:v:11:y:2022:i:2:p:1-16. 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.