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

Fateh Bougamouza and Samira Hazmoune

International Journal of Data Analysis Techniques and Strategies, 2024, vol. 16, issue 3, 341-362

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.

Keywords: sentiment analysis; Algerian Arabic dialect; DziriBERT; transfer learning; Algerian telephone operators; emoji categorisation. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=140647 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:16:y:2024:i:3:p:341-362

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().