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

Abdelghani BOUZIANE; Benamar BOUOUGADA; Djelloul BOUCHIHA; Noureddine DOUMI

doi:10.18540/jcecvl10iss7pp20058

Authors

Abdelghani BOUZIANE Department of Computer Science, Institute of Sciences, University centre of Naama, Algeria
Benamar BOUOUGADA Department of Computer Science, Institute of Sciences, University centre of Naama, Algeria https://orcid.org/0009-0006-4020-4093
Djelloul BOUCHIHA University Centre of Naama
Noureddine DOUMI Department of Computer Science, University of Saida, Algeria

DOI:

https://doi.org/10.18540/jcecvl10iss7pp20058

Keywords:

Sentiment analysis, Artificial intelligence, Social Web evolution, Deep learning solutions, Bi-LSTM

Abstract

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.