YouTube Comment Sentiment Analysis Web App

This web application built with Flask analyzes comments from a YouTube video. It extracts comments using the YouTube Data API, performs sentiment analysis on them, generates a word cloud, and calculates precision and recall metrics.

Setup

1. Clone Repository: Clone this repository to your local machine:

   git clone https://github.com/your_username/your_repository.git

2. Navigate to Directory: Navigate to the project directory:

   cd your_repository

3. Install Dependencies: Install the required dependencies listed in requirements.txt:

   pip install -r requirements.txt

4. Obtain API Key: Obtain a YouTube Data API key from the Google Developers Console and replace 'YOUR_API_KEY' in app.py with your actual API key.

Usage

1. Run Application: Run the Flask application:

   python app.py

2. Access Web Interface: Open a web browser and go to http://localhost:5000 to access the web interface.

3. Enter Video URL: Enter the URL of the YouTube video you want to analyze and submit the form.

4. View Analysis: Wait for the analysis to complete. Once finished, the web page will display the word cloud of comments, the most positive and negative comments, and precision and recall metrics.

File Structure

• app.py: Contains the Flask application code including routes and analysis functions.
• templates/: Directory containing HTML templates for rendering the web pages.
• static/: Directory for storing static files such as CSS stylesheets and client-side scripts.

Credits

• Flask: Web framework for Python.
• YouTube Data API: API for fetching YouTube data.
• TextBlob: Python library for processing textual data.
• WordCloud: Python library for creating word clouds.
• Matplotlib: Python plotting library for visualization.
• Bootstrap: Front-end component library for designing responsive web pages.

License

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