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🎬 Movie Review Sentiment Analyzer

📝 Overview

The Movie Review Sentiment Analyzer is a machine learning-powered application that classifies movie reviews as Positive, Negative, or Neutral based on sentiment analysis. Using a trained Recurrent Neural Network (RNN), the app predicts the sentiment of user-inputted text and provides a confidence score.

✨ Features

  • 📝 Text-based Sentiment Analysis
  • 📊 Confidence Score for Sentiment Prediction
  • 🎭 Emoji-based Sentiment Representation
  • 🎨 Modern, User-Friendly UI with Streamlit
  • 📉 Visual Progress Bar for Sentiment Strength

🛠 Technology Stack

  • Python
  • Streamlit
  • TensorFlow/Keras
  • NumPy & Pandas
  • RNN

🚀 Getting Started

Prerequisites

  • Python 3.8+
  • TensorFlow installed
  • Pretrained model (SimpleRnn_imdb.h5)
  • Tokenizer file (tokenizer.pkl)

Installation

  1. Clone the repository
git clone https://github.com/taskmaster-1/sentiment-analyzer.git
cd sentiment-analyzer
  1. Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
  1. Install dependencies
pip install -r requirements.txt

Running the App

streamlit run app.py

📦 Requirements File

Create a requirements.txt with:

streamlit
tensorflow
numpy
pandas
pickle-mixin

🤝 Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

🔒 Security

  • Never commit your model files (SimpleRnn_imdb.h5) or tokenizer files (tokenizer.pkl) to public repositories
  • Use .gitignore to exclude sensitive files

📊 How It Works

  1. Enter a movie review in the text box
  2. Click the "Analyze Sentiment" button
  3. The model predicts the sentiment (Positive, Negative, or Neutral)
  4. The confidence score is displayed
  5. A visual progress bar shows sentiment strength

📈 Future Roadmap

  • Improve model accuracy with advanced NLP techniques
  • Deploy as a web app using Streamlit Sharing or Render
  • Support for multiple languages
  • Live API for sentiment prediction

🏷️ Versioning

Current Version: 1.0.0

📄 License

Distributed under the MIT License. See LICENSE for more information.

🌐 Deployed Application

🔗 Live

📞 Contact

Gmail - vivekyad5223@gmail.com

Project Link: https://github.com/taskmaster-1/sentiment-analyzer

🙏 Acknowledgements