CommentIQ

AI-Powered Feedback Processing with Sentiment Analysis & Critical Alerts

Automatically analyze user feedback with:

• 😊 Sentiment rating (Positive/Neutral/Negative)
• 📝 AI-generated summaries
• 🚨 Critical keyword detection
• 📂 Airtable integration

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Quick Start

1. Clone & Install

git clone https://github.com/your-username/feedback-analyzer.git
cd feedback-analyzer
pip install -r requirements.txt

2. Add to .env

AIRTABLE_BASE_ID=your_base_id
AIRTABLE_API_KEY=your_key
HUGGING_FACE_API_KEY=your_hf_key

3. Run

streamlit run app.py

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Key Features:

• Instant Analysis: Get sentiment, summary, and category in one click
• Security Alerts: Flags 15+ critical terms like "data breach" or "hack"
• Smart Storage: Auto-saves to Airtable with metadata
• AI Models: Hugging Face's BERT (sentiment) & Pegasus (summarization)

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Setup Guide:

1. Airtable: Create table named Feedback with fields:
   Feedback, Sentiment, Summary, Category, Flagged Keywords
2. Hugging Face: Get API token from Account Settings

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MIT Licensed • For production use, add error handling and rate limiting.