🍽️ Restaurant Review Analysis

🚀 A Machine Learning project that analyzes restaurant reviews and predicts sentiment (Positive/Negative) using NLP techniques.

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🧠 Project Overview

This project processes customer reviews and applies Natural Language Processing (NLP) and Machine Learning to classify sentiments.

🔍 Helps businesses understand:

• Customer satisfaction
• Common complaints
• Overall sentiment trends

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⚙️ Tech Stack

• 🐍 Python
• 📊 Pandas
• 🤖 Scikit-learn
• 🧠 NLTK
• 🪄 NLP (Text Cleaning, Tokenization, Stemming)
• 🎯 Machine Learning (Naive Bayes)

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🔥 Features

• ✅ Text preprocessing (stopwords removal, stemming)
• ✅ Sentiment classification using ML model
• ✅ GUI support using Tkinter
• ✅ Train & test on custom datasets

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📁 Project Structure

├── Review.py                # Main application
├── Restaurant_Reviews.txt  # Training dataset
├── test_data_reviews.txt   # Test dataset
├── Untitled.ipynb          # Experimentation (Jupyter)
├── README.md

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🚀 How to Run

1️⃣ Install dependencies

pip install pandas scikit-learn nltk

2️⃣ Run the application

python Review.py

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📊 Model Details

• Algorithm: Multinomial Naive Bayes
• Feature Extraction: CountVectorizer
• NLP Techniques:
  • Tokenization
  • Stopword Removal
  • Stemming

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📸 Output

👉 Predicts whether a review is Positive 👍 or Negative 👎

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🌟 Future Improvements

• 🔹 Add Deep Learning (LSTM / BERT)
• 🔹 Deploy as Web App
• 🔹 Improve accuracy with larger datasets

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