Résumé:
Sentiment analysis is a key area of natural language processing that aims to automatically identify and interpret opinions and emotions expressed in text. With the growing volume of user-generated content on social media, analyzing sentiment has become increasingly valuable for researchers and organizations.
This study focuses on sentiment analysis in Arabic, with a particular emphasis on dialectal variations. Due to the complexity of the Arabic language-its morphology, diverse dialects, and lack of annotated resources this task presents unique challenges. The work explores various text representation techniques and evaluates a range of machine learning and deep learning models to determine suitable approaches for Arabic sentiment classification.
Through systematic experimentation, this research highlights the impact of text encoding methods and model choice on sentiment classification performance in Arabic, offering insights for future studies in this under-resourced linguistic domain.
Keywords: Sentiment Analysis, Machine learning, Deep learning,.
