Sentiment Analysis on Algerian Dialect with Transformers

Abstract

The task of extracting sentiment from text has been widely studied in the field of natural language processing. However, little work has been conducted specifically on the Arabic language with the Algerian dialect. In this research, we aim to make a significant contribution to the field of sentiment analysis on the Algerian dialect by creating a custom and relatively large dataset with a tailored deep learning model. The dataset was extracted from Algerian YouTube channels and manually annotated by the research team. We then utilize this dataset to train a state-of-the-art deep learning model for natural language processing called BERT, which is a type of Transformer model. Using this model, we were able to achieve an F1-score of 78.38% and an accuracy of 81.74% on the testing set. This demonstrates the effectiveness of our approach and the potential of using BERT for sentiment analysis on the Algerian dialect. Our model can be used to infer sentiment from any Algerian text, thus providing a valuable tool for understanding the opinions and emotions of the population. This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area.