Amazon-Product-Reviews-Egyptian-Sentiment-Analysis

Amazon Product Reviews Sentiment Analysis (Egyptian Arabic)

? Project Description

This project focuses on building a complete sentiment analysis pipeline for Amazon product reviews written in Egyptian Arabic dialect.

The work covers the full NLP workflow starting from data collection, passing through text preprocessing, feature engineering, and ending with training and evaluating a machine learning model for sentiment classification.

The goal of the project is to accurately understand customer opinions expressed in Egyptian Arabic, which is often challenging due to slang, informal spelling, and dialectal variations.

⚙️ Project Implementation Stages

1️⃣ Data Collection

Collected Amazon product reviews dataset written in Arabic.

Filtered and prepared the data to focus on Egyptian dialect reviews.

Organized reviews with their corresponding sentiment labels.

2️⃣ Data Cleaning & Preprocessing

Removed noise such as punctuation, special characters, numbers, and URLs.

Normalized Arabic text (unifying characters like أ / ا / إ).

Removed stopwords specific to Arabic.

Tokenized text to prepare it for feature extraction.

3️⃣ Feature Engineering

Converted text data into numerical representations using TF-IDF.

Experimented with different n-gram configurations to capture contextual sentiment patterns.

4️⃣ Model Building

Trained a Support Vector Machine (SVM) classifier for sentiment analysis.

Tuned model parameters to improve classification performance on dialectal Arabic text.

5️⃣ Model Evaluation

Evaluated the model using accuracy and classification metrics.

Analyzed model behavior across different sentiment classes.

?️ Technologies & Tools

Python

scikit-learn

NLTK

pandas & NumPy

TF-IDF Vectorization

SVM Classifier

? Project Outcomes

Built a reliable sentiment analysis model for Egyptian Arabic reviews.

Demonstrated the full NLP lifecycle from raw text to trained ML model.

Highlighted the challenges and solutions when working with Arabic dialects in NLP tasks.

? Use Cases

Understanding customer satisfaction from Arabic product reviews.

Supporting e-commerce decision-making using sentiment insights.

Academic and applied NLP research on Arabic dialects.