Yelp's AI Pipeline for Inappropriate Language Detection in User Reviews Background Using OpenAI
This project replicates a modern AI moderation pipeline using OpenAI's GPT-4 to automatically flag and classify inappropriate or harmful content in user-generated reviews. It's inspired by large-scale systems like Yelp’s internal moderation workflow and is adaptable for various platforms such as e-commerce, social media, forums, or SaaS communities.
🧠 What It Does
This tool detects nuanced, offensive, or policy-violating content in text-based reviews, with a focus on:
Profanity and explicit language
Sarcasm or metaphorical abuse
Implicit hate speech or discriminatory tones
Sexual innuendo or veiled threats
Harassment, personal attacks, and lewd remarks
It uses GPT-4's advanced understanding of natural language to go beyond simple keyword filters or regular expressions.
⚙️ Features
✅ Zero-shot detection using OpenAI GPT models
✅ Handles sarcasm, implicit bias, and indirect toxicity
✅ Plug-and-play design for integration into existing pipelines
✅ Built with the latest OpenAI Python SDK (>=1.0.0)
✅ Lightweight and easy to customize for different moderation policies
✅ Detects offensive, sarcastic, or implicit toxic content
✅ Supports nuanced language and contextual abuse detection
✅ Easy to adapt to any review-based or text-heavy platform
✅ CLI-friendly and lightweight for integration into larger pipelines
macOS/Linux
export OPENAI_API_KEY=your_key_here
Windows CMD
set OPENAI_API_KEY=your_key_here
📤 Output Sample
The script returns a structured dictionary response with a flag and category prediction. For example:
{
"flagged": true,
"category": "Implicit Hate Speech",
"reason": "Suggests differential treatment based on race or background."
}
{
"flagged": true,
"category": "Sexual Innuendo",
"reason": "Contains implicit sexual content or suggestive language."
}
🔮 Future Improvements
Add confidence scores or basic explainability for classification decisions
Integrate a Streamlit or FastAPI UI for real-time moderation dashboards
Fine-tune on domain-specific datasets for industry-specific accuracy
Extend support for multilingual input and global content moderation