Retail Sentiment Intelligence Platform
Multi-Source NLP Monitoring with Real-Time Brand Alerting
Project Overview
A consumer goods brand was entirely reactive to customer feedback — learning about product defects from return spikes, not from monitoring signals. They needed a unified intelligence layer across reviews, social, and support to catch problems weeks earlier.
The Challenges
- 1
Aggregating live data from 5 different APIs with inconsistent rate limits
- 2
Multi-aspect sentiment needed (product, packaging, delivery, support) — not just positive/negative
- 3
Distinguishing genuine product signal from promotional noise and bots
- 4
Alert thresholds required calibration to avoid overwhelming the team
Our Approach
We built a Python ETL pipeline pulling from Amazon reviews, Twitter/X, Zendesk, Instagram, and the App Store. We fine-tuned a Hugging Face BERT model for multi-aspect sentiment classification, deployed a React dashboard for live monitoring, and added a Celery worker for weekly AI-generated digest emails.
Key Features & Metrics
Multi-source ingestion: Amazon, Twitter/X, Zendesk, Instagram, App Store
Aspect-level sentiment scores for product, packaging, delivery, and support
Real-time alerting when any sentiment dimension drops below configured threshold
Trending issue detection via keyword clustering on negative feedback
Weekly executive digest email with AI-generated commentary and action items
Brand insight extraction 74% faster than the previous manual process
Results & Business Outcome
The team detected a packaging defect 3 weeks before return rates spiked — saving an estimated $90K in logistics costs. Manual monitoring headcount reduced by 2 FTEs.
Your customers are already telling you what is wrong with your product. The only question is whether you are listening fast enough to act on it.
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