YouTube Content Guidelines Analyzer

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

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📌 Table of Contents

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

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✨ 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

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⚙️ 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

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🚀 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"

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🔧 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.

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🤖 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

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🤝 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

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📜 License

This project is licensed under MIT License.

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📊 Example Output

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💡 Need Help?

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

🚀 Happy Analyzing! 🚀