YoutubeNLP

A Streamlit application for analyzing YouTube video comments using Natural Language Processing techniques.

Screenshots

Main Screen

Language Distribution

Sentimental Analysis

Features

• YouTube Comment Analysis: Automatically fetch and analyze comments from any YouTube video URL
• Sentiment Analysis: Evaluate the emotional tone of comments (positive, negative, or neutral)
• Keyword Extraction: Identify the most important words and topics in comments
• Interactive Visualizations:
  • Sentiment distribution chart
  • Comment length vs. sentiment scatter plot
  • Interactive word cloud with sentiment filtering
  • Top keywords bar chart
  • Sentiment over time analysis (when available)
  • Named entity recognition and analysis
  • Language detection and language-specific insights
• Data Export: Download all analysis results as CSV for further research

Requirements

• Python 3.7+
• Required packages listed in requirements.txt
• Optional: Google Cloud Language API credentials for enhanced NLP capabilities

Installation

1. Clone this repository:

git clone https://github.com/murapa96/YoutubeNLP.git
cd YoutubeNLP

2. Run the setup script to install dependencies and download required models:

python setup.py

This script will:

• Install required packages from requirements.txt
• Download the necessary spaCy model using python -m spacy download en_core_web_sm
• Download required NLTK resources
• Set up necessary directories
• Check for Google Cloud credentials

3. (Optional) Set up Google Cloud Language API for enhanced analysis:
   • Create a project in Google Cloud Console
   • Enable the Cloud Natural Language API
   • Create and download service account credentials
   • Set the environment variable: export GOOGLE_APPLICATION_CREDENTIALS=/path/to/credentials.json

Usage

1. Run the Streamlit app:

streamlit run app.py

2. Open your browser at the URL provided in the terminal (usually http://localhost:8501)
3. Enter a YouTube video URL and click "Analyze Comments"
4. Explore the interactive visualizations and insights

Technical Information

• Fallback Mechanisms: Falls back to TextBlob for sentiment analysis if Google Cloud credentials aren't available
• Multi-language Support: Automatically detects language and applies appropriate analysis
• Named Entity Recognition: Identifies people, organizations, locations, and other entities in comments
• Customizable: Analysis options allow for comment count and sorting preferences
• Efficient Processing: Uses batched processing for better performance with large comment volumes

Credits

Created by Pablo Ramos Muras

License

This project is licensed under the terms of the license included in the repository.

Contributing

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