Machine learning-Based Approach to Annotate Social Media Comments for Product Recommendation

Abstract

Sentiment analysis is a core task in Natural Language Processing (NLP), widely applied in sectors such as digital marketing, politics, and customer feedback systems. However,

its application to Arabic dialects—particularly the Algerian dialect—remains challeng- ing due to linguistic diversity, lack of standardized orthography, and the prevalence of

non-standard scripts like Arabizi. Existing works are often limited to Modern Standard Arabic or fail to effectively capture dialect-specific nuances.

To address these limitations, this work proposes a late fusion architecture that com- bines contextual embeddings from two complementary pre-trained models: DziriBERT,

specialized for Algerian dialect, and MARBERT, trained on a broad set of Arabic di- alects. The fused representations are then passed to a BiLSTM classifier, selected for

its strong capacity to capture sequential context. Experiments conducted on two real-world datasets—Djezzy and BrandDZ—demonstrated

the effectiveness of this approach. The BiLSTM-based late fusion model achieved ac- curacies of 0.83% , respectively, These results confirm that integrating dialectal and

broader Arabic representations, along with appropriate sequence modeling, leads to more robust and reliable sentiment classification for under-resourced dialects such as Algerian Arabic.