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Sentiment analysis of customer reviews for Algerian dialect using the DziriBERT model

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  • Fateh Bougamouza
  • Samira Hazmoune

Abstract

The increasing volume of daily comments and tweets presents a valuable resource for improving various processes, from business strategies to service management. However, the Algerian Dialect (AlgD), despite its growing presence on social media, has been overlooked in sentiment analysis. This study addresses this gap by proposing an approach for sentiment analysis of Algerian Dialect feedback, specifically from customers of Algerian telephone operators (Djezzy, Mobilis, and Ooredoo). Leveraging transfer learning, the pre-trained DziriBERT model was fine-tuned, with experiments refining data preprocessing techniques and hyperparameters. The outcome is an impressive 82.01% accuracy rate, offering promising insights into sentiment analysis in the Algerian Dialect and highlighting its potential significance for companies and researchers in the field.

Suggested Citation

  • Fateh Bougamouza & Samira Hazmoune, 2024. "Sentiment analysis of customer reviews for Algerian dialect using the DziriBERT model," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 16(3), pages 341-362.
    • Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:341-362
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