Project overview

This project aims to create an Arabic sentiment analysis system, that takes advantage of the different text representation models like TF-IDF, Bag of words, and Bag of concepts in addition to exploring newer methods such as appraisal theory.

Architecture

The general architecture of the system is the following:

Repository layout

The code for the system is organized into the following branches:

1. AJGT: Code for Arabic sentiment analysis using classical machine learning models built using the AJGT dataset.
2. ASTC: Code for Arabic sentiment analysis using classical machine learning models built using the ASTC dataset.
3. ASTD: Code for Arabic sentiment analysis using classical machine learning models built using the ASTC dataset.
4. LABR: Code for Arabic sentiment analysis using classical machine learning models built using the ASTC dataset.
5. DL: Code for Arabic sentiment analysis using deep learning.
6. Appraisal: Code for Arabic sentiment analysis using appraisal features.
7. Deployment: Deployment of the system using Streamlit.

Each branch contains a details overview of the dataset used, as well as all the performance metrics.

Datasets

• LABR (Large scale Arabic Book Reviews)
• AJGT (The Arabic Jordanian General Tweets)
• ASTC (Arabic Sentiment Twitter Corpus)
• ASTD (Arabic Sentiment Tweets Dataset)

Text representation models

The system supports the following text representation models:

• Bow (Bag of Words)
• TF-IDF (Term frequency, inverse document frequency)
• LSA (Latent semantic analysis)
• LDA (Latent Dirichlet allocation)
• BoC (Bag of Concepts)
• Appraisal groups

Machine learning models

The previous text representation modes are used to create features for the following models:

• Naive bayes
• Logistic regression
• Support Vector Machine
• Random forest

As for deep learning, we opted for the BERT (Bidirectional Encoder Representations) model and its variants.