Etudiant : JBILOU ZAKARIAE

Filière : LF Sciences Mathématiques et Informatique

Encadrant : Pr. NFAOUI EL HABIB

Annèe : 2023

Résumé : Sentiment analysis is the process of determining whether a text or a writing is positive, negative. A lot of research has been done to improve the accuracy of sentiment analysis methods, varying from simple linear models to more complex deep neural network models. Lately, the transformer-based model showed great success in sentiment analysis and was considered as the state-of-the-art model for various languages (English, German, French, Turk, Arabic, etc.). However, the accuracy for Arabic sentiment analysis still needs improvements especially in tokenization level during data processing. In fact, the Arabic language imposes many challenges, due to its complex structure, various dialects, and resource scarcity. The improvement of the proposed approach consists of integrating an Arabic BERT tokenizer instead of a basic BERT Tokenizer. Furthermore, we have deployed the developed model into production by integrating it into a Django WebApp. This integration allows for seamless utilization by clients, makingthe sentiment analysis model easily accessible and user-friendly. By combining advanced tokenization techniques and deploying the model in a practical web application, we aim to enhance the accuracy and usability of sentiment analysis for Arabic texts.