Sentiment analysis of Algerian Arabic dialect on social media Using Bi-LSTM recurrent neural networks

Résumé

This paper presents a sentiment analysis approach using Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Networks to train predictive models for sentiment analysis on social media, particularly focusing on Algerian Arabic Dialect. The method leverages word-to-vector embedding for word representation and incorporates natural language understanding of emojis to improve semantic interpretation. The model achieves a high accuracy of 94%, demonstrating its effectiveness in analyzing sentiments in online discussions. The originality lies in applying Bi-LSTM to handle multilingual challenges on social platforms. The findings have practical implications for business, policymaking, and public sentiment evaluation, while also contributing positively to fostering informed online discourse.