Sentiment Analysis of Arabic Algerian Dialect

Abstract

This study addresses the scarcity of resources in Natural Language Processing (NLP) for the Algerian dialect, particularly in the context of scientific discourse. We have constructed a sentiment-labeled dataset comprising sentences related to scientific research, annotated across three languages: English, Modern Standard Arabic, and Algerian Arabic. Each sentence is tagged with its corresponding sentiment polarity (positive, negative, or neutral). The dataset aims to facilitate the development of domain-specific sentiment analysis models tailored to the Algerian dialect. Preliminary experiments utilizing transformer-based models, such as BERT variants fine-tuned on this dataset, demonstrate promising results in accurately classifying sen timent within this under-resourced dialect. This work contributes to the advancement of NLP tools for the Algerian dialect and underscores the importance of creating specialized resources for low-resource languages.