Skip to content

README.md

Latest commit

 

History

History
173 lines (128 loc) · 4.22 KB

README.md

File metadata and controls

173 lines (128 loc) · 4.22 KB

YouTube Content Guidelines Analyzer

🚀 A comprehensive tool to analyze YouTube titles, descriptions, and thumbnails for policy compliance, demonetization risks, and content violations.

📌 Table of Contents

  1. Features
  2. Installation
  3. Usage
  4. Configuration
  5. How It Works
  6. Contributing
  7. License

✨ Features

✅ Text Analysis

  • Scans titles & descriptions for policy violations
  • Detects hate speech, violence, harassment, sexual content, misinformation, and copyright issues
  • Provides severity ratings and suggested fixes

✅ Sentiment Analysis

  • Uses TextBlob to determine if content is positive, negative, or neutral

✅ Image Analysis (Thumbnails)

  • OCR to extract text from images
  • Skin detection to flag potential nudity
  • Checks for inappropriate visuals

✅ Machine Learning (LSTM Model)

  • Predicts risk scores based on historical data
  • Improves accuracy over time

✅ YouTube API Integration

  • Analyze existing videos by ID
  • Fetch metadata for deeper insights

✅ Multilingual Support

  • Supports non-English content via Google Translate

✅ Historical Trend Tracking

  • Tracks risk scores over time
  • Visualizes compliance trends

✅ User-Friendly GUI

  • Built with Tkinter
  • Easy-to-use interface for non-technical users

⚙️ Installation

Prerequisites

  • Python 3.8+
  • Tesseract OCR (for image text extraction)

Step 1: Clone the Repository

git clone https://github.com/Harsha-hue/YouTube_Content_Guidelines_Analyzer.git
cd youtube-guidelines-analyzer

Step 2: Install Dependencies

pip install -r requirements.txt
python -m spacy download en_core_web_lg

Step 3: Install Tesseract OCR

  • Windows: Download from GitHub
  • Mac: 
    brew install tesseract
  • Linux (Debian/Ubuntu): 
    sudo apt install tesseract-ocr

Step 4: Get a YouTube API Key

  1. Go to Google Cloud Console
  2. Create a project & enable YouTube Data API v3
  3. Generate an API key
  4. Replace YOUR_API_KEY in main.py

🚀 Usage

1. Run the Application

python main.py

2. GUI Workflow

  • Enter video Title and Description
  • Upload a thumbnail (optional)
  • Click "Analyze"
  • View detailed report

3. Command-Line Options

# Analyze text only
python main.py --title "Your Video Title" --desc "Your Description"

# Analyze a YouTube video by ID
python main.py --video-id "VIDEO_ID"

# Analyze non-English text
python main.py --text "Foreign Text" --lang "es"

🔧 Configuration

1. Custom Keywords & Triggers

Edit demonetization_triggers.csv to add/remove terms.

2. Retrain ML Model

python train_model.py --data "your_dataset.csv"

3. Change Language Support

Modify target_lang in translate_and_analyze() for different languages.

🤖 How It Works

  1. Text Analysis

    • Uses regex & NLP to detect violations
    • Checks sentiment (positive/negative/neutral)

  2. Image Analysis

    • OCR extracts text
    • OpenCV detects skin tones

  3. Machine Learning

    • LSTM model predicts risk scores
    • Improves with user feedback

  4. Historical Trends

    • Stores past analyses
    • Plots risk trends over time

🤝 Contributing

  1. Fork the repo
  2. Create a branch (git checkout -b feature/new-feature)
  3. Commit changes (git commit -m "Add new feature")
  4. Push (git push origin feature/new-feature)
  5. Open a Pull Request

📜 License

This project is licensed under MIT License.

📊 Example Output

GUI Screenshot

💡 Need Help?

Open an Issue or reach out at harshavardhankarne@gmail.com.

🚀 Happy Analyzing! 🚀