README.md

Letterboxd Film Tracker 🎬

A web application for analyzing movie reviews and sentiment from Letterboxd. This tool allows users to scrape reviews, analyze sentiment, and visualize trends in movie reception over time.

Features ✨

• Review Scraping: Scrape up to 500 reviews (5000 with admin privileges) from any movie on Letterboxd
• Sentiment Analysis: Analyze the emotional tone of reviews using NLTK
• Data Visualization: Interactive charts and graphs showing:
  • Rating distributions
  • Sentiment trends over time
  • Overall movie statistics
  • User engagement patterns
• Real-time Processing: All analyses are performed in real-time
• Movie Information: Display comprehensive movie details including director, release date, and average rating

Technologies Used 🛠️

• Frontend: Streamlit
• Database: MongoDB Atlas
• Data Processing:
  • Python
  • Beautiful Soup
  • NLTK
• Data Visualization:
  • Matplotlib
  • Plotly

Installation & Setup 🚀

1. Clone the repository:

git clone https://github.com/juanjuanjuanfer/yet_to_be_named.git
cd yet_to_be_named

2. Create a virtual environment and activate it:

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

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:
   • Create a .streamlit/secrets.toml file with your MongoDB connection string:

MONGODB_URI = "your_mongodb_connection_string"

5. Run the application:

streamlit run Home.py

Project Structure 📁

lbxd/
├── Home.py                  # Main Streamlit application
├── pages/                   # Additional pages
│   ├── Scraper.py           # Movie review scraping page
│   └── Dashboard.py         # Analytics dashboard
├── utils.py                 # Utility functions
├── connection_mongo.py      # Database operations  
├── film.py                  # Film scrpaer
├── user.py                  # User scraper
└── requirements.txt         # Project dependencies

Usage 📖

1. Home Page: Introduction to the application and its features
2. Scraper Page:
   • Enter a Letterboxd movie URL or title
   • Select number of reviews to scrape
   • View scraping progress in real-time
3. Dashboard Page:
   • Select a movie from the database
   • View various analytics and visualizations
   • Filter data by date range
   • Analyze sentiment distribution

Related Projects 🔗

• PyBoxd - Python package for Letterboxd scraping

Contributors 👥

• Alexis Canto Ancona (2009020@upy.edu.mx)
• Samantha Castro Echeverria (2109028@upy.edu.mx)
• Christopher Cumi Llanes (2109048@upy.edu.mx)
• Juan Fernandez Cruz (2109061@upy.edu.mx)
• Juliana Ramayo Cardoso (2109128@upy.edu.mx)

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments 🙏

• Letterboxd for providing a platform that inspired this project
• Universidad Politécnica de Yucatán for supporting this academic project
• All contributors and users who have helped improve this tool

Disclaimer ⚠️

This project is not officially affiliated with Letterboxd. It is an academic project created for educational purposes. Please be mindful of Letterboxd's terms of service when using this tool.