🎬 Movie Sentiment Analyzer

Live Demo: https://moviesentiment-analyzer.streamlit.app/

A machine learning web application built with Streamlit that analyzes the sentiment of movie reviews. It predicts whether a review is Positive 😊 or Negative 😞 using a variety of classic machine learning and deep learning models.

✨ Features

• Multiple Models: Choose from Logistic Regression, Decision Tree, Random Forest, RNN, and LSTM to analyze reviews.
• Model Comparison: Compare the performance and predictions of all models side-by-side.
• Interpretability:
  • Linear Models: Highlights the exact words that influenced the prediction (positive or negative impact).
  • Tree Models: Displays the global top 20 most important features.
• Interactive UI: Simple, user-friendly Streamlit interface with real-time confidence scores.

🛠️ Tech Stack

• Frontend: Streamlit
• Machine Learning: Scikit-Learn (Logistic Regression, Decision Trees, Random Forests)
• Deep Learning: PyTorch (RNN, LSTM)
• Data Processing: Pandas, NumPy
• Visualization: Matplotlib, Seaborn, Wordcloud

📁 Project Structure

movie_sentiment_analysis/
│
├── app/
│   └── main.py              # Streamlit application
├── models/                  # Pre-trained models (.joblib, .pt)
├── notebooks/               # Jupyter notebooks for EDA and Model Training
│   ├── 01_data_inspection_and_eda.ipynb
│   └── machine_learning_models.ipynb
├── data/                    # Raw and processed datasets
│   ├── raw/
│   └── processed/
├── src/                     # Helper scripts
│   └── download_models.py
├── pyproject.toml           # Project configuration
└── requirements.txt         # Project dependencies

🚀 Getting Started

Prerequisites

Ensure you have Python >= 3.12 installed.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/movie_sentiment_analysis.git
   cd movie_sentiment_analysis

2. Create a virtual environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install the dependencies:

   pip install -r requirements.txt

Running the App

To start the Streamlit web application, run the following command from the root directory:

streamlit run app/main.py

The app will automatically open in your default web browser at http://localhost:8501.

🧠 Models Available

This application dynamically loads machine learning models from Hugging Face Hub (Repository: monusharma21/portfolio).

When you start the application, it will automatically download the required model files if they are not already present in the models/ directory:

• logistic_model.joblib
• decision_tree_model.joblib
• random_forest_model.joblib
• rnn_model.pt
• lstm_model.pt

This setup keeps the repository lightweight and makes deployment straightforward!

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is open source and available under the MIT License.