IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-031-75329-9_39.html
   My bibliography  Save this book chapter

Sentiment Analysis in Moroccan Dialect via Arabic Transcoding: Evaluating Different Machine Learning Strategies

In: Information Systems and Technological Advances for Sustainable Development

Author

Listed:
  • Sakhi Hasnae

    (Hassan II University)

  • Sanaa Filali

    (Hassan II University)

Abstract

This research aims to improve sentiment analysis for the Moroccan dialect (Darija) by converting it into Modern Standard Arabic (MSA). Darija poses unique challenges for natural language processing (NLP) due to the lack of extensive computational resources. To address this, we utilized an existing Arabic dictionary and developed a supplementary dictionary for specific Darija words significantly different from MSA. This approach allows us to leverage the robust tools and resources available for MSA, optimizing time and enhancing sentiment analysis effectiveness. Our study involves a comparative analysis of three classical machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Testing these models on the MAC Dataset, both before and after applying our conversion method, revealed significant improvements in accuracy, demonstrating the potential of our approach.

Suggested Citation

  • Sakhi Hasnae & Sanaa Filali, 2024. "Sentiment Analysis in Moroccan Dialect via Arabic Transcoding: Evaluating Different Machine Learning Strategies," Lecture Notes in Information Systems and Organization, in: Mohamed Ben Ahmed & Anouar Abdelhakim Boudhir & Hany Farhat Abd Elhamid Attia & Adriana Eštoková & M (ed.), Information Systems and Technological Advances for Sustainable Development, pages 354-361, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-75329-9_39
    DOI: 10.1007/978-3-031-75329-9_39
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnichp:978-3-031-75329-9_39. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.