🎬 Review Analysis System

A Streamlit web app for analyzing movie reviews and classifying them as positive or negative using a pre-trained RNN model built with TensorFlow and Keras on the IMDb dataset.

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📚 Project Overview

This project allows users to input any movie review text and receive a real-time sentiment prediction.
It uses a simple Recurrent Neural Network (RNN) trained on the IMDb movie review dataset.
The web interface is built with Streamlit for a fast, clean, and easy user experience.

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🚀 Features

• Text Input: Users can input any movie review.
• Sentiment Prediction: Classifies the review as Positive or Negative.
• Prediction Score: Shows the model's confidence score for the prediction.
• Real-time Analysis: Quick response via a lightweight Streamlit interface.

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🛠️ Technologies Used

• Python
• TensorFlow / Keras
• Streamlit
• IMDb Dataset (Keras built-in)

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🧠 How It Works

• The app loads a pre-trained RNN model (simple_rnn_imdb.h5).
• User input is preprocessed by:
  • Tokenizing words based on IMDb's word index.
  • Padding the sequence to a fixed length (500).
• The processed text is passed to the model for sentiment prediction.
• Displays whether the review is Positive or Negative along with the confidence score.

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⚙️ How to Run Locally

1. Clone this repository:

   git clone https://github.com/your-username/review-analysis-system.git
   cd review-analysis-system

2. Install the required packages:

   pip install tensorflow streamlit

3. Make sure you have the pre-trained model file:

   • simple_rnn_imdb.h5 should be present in the project folder.

4. Run the Streamlit app:

   streamlit run app.py

5. Open the app in your browser at:

   http://localhost:8501
   

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📂 Project Structure

├── app.py                  # Main Streamlit application
├── simple_rnn_imdb.h5       # Pre-trained RNN model
└── README.md                # Project documentation

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📢 Future Improvements

• Add live review scraping (e.g., from Rotten Tomatoes or IMDb).
• Improve preprocessing with punctuation removal and stopword filtering.
• Deploy online using platforms like Streamlit Cloud or AWS.

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🤝 Contributing

Pull requests are welcome!
If you find any bugs or want to add features, feel free to open an issue or submit a pull request.

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📜 License

This project is open source under the MIT License.

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🌟 Show Your Support

If you like this project, please ⭐ star the repo — it really helps!