Restaurant Reviews Sentiment Analysis

Project Overview

This project is an NLP (Natural Language Processing) application that analyzes restaurant reviews and classifies them as either:

Positive (موجب) ?

Negative (سالب) ?

The main goal of the project is to help restaurant owners and customers understand public opinion by automatically evaluating written reviews.

? Project Objective

Analyze text reviews written by customers

Determine the sentiment of each review (Positive / Negative)

Apply NLP techniques to convert text into a format understandable by machine learning models

? Technologies & Concepts Used

Natural Language Processing (NLP)

Text Preprocessing

Tokenization

Stopwords Removal

Text Cleaning

Feature Extraction

Bag of Words (BoW) / TF-IDF

Machine Learning Algorithms

Logistic Regression / Naive Bayes / SVM (حسب البروجكت)

?️ Tools & Libraries

Python ?

NLTK / SpaCy

Scikit-learn

Pandas & NumPy

? Dataset

The dataset consists of restaurant reviews written in text format

Each review is labeled as:

1 → Positive Review

0 → Negative Review

⚙️ How It Works

Load and preprocess the dataset

Clean the text (remove punctuation, stopwords, etc.)

Convert text into numerical features

Train the machine learning model

Predict sentiment of new reviews

✅ Example

Input Review:

"The food was amazing and the service was excellent"

Output: ✔️ Positive Review

? Future Improvements

Add Neutral sentiment

Support Arabic reviews

Deploy the model as a web application

Improve accuracy using deep learning models

?‍? Author

Ahmed Elrouby NLP & Machine Learning Student

⭐ Notes

This project was developed for learning and practicing Natural Language Processing concepts and applying them to real-world data.