Sentiment Analyzer
Deployed a real-time NLP sentiment analysis engine across Play Store & App Store reviews, Slack, and Intercom — surfacing product signals in one unified dashboard.
Data Sources
Insight Acceleration
Rating Boost
The Challenge
A leading mobile application publisher with over 20 active apps struggled to gather qualitative feedback. User feedback was scattered across App Store reviews, Google Play Store, Intercom tickets, and social mentions, making it impossible for product managers to build a unified features roadmap.
Our Solution
We designed a multi-channel scraper and NLP analysis system. It aggregates reviews, translates multi-language messages, and classifies feedback based on emotions (frustrated, delighted, neutral) and topics (billing, UX, bugs). The outputs are visible in an interactive dashboard showing emerging issues in real-time.
Interactive System Architecture
Ingestion Pipelines
Constructed serverless polling scrapers that retrieve data from app stores, Intercom, and Zendesk.
Transformer Pipeline
Configured fine-tuned BERT models for multi-label emotion and topic classification.
Real-time Feed
Built a React + WebSockets dashboard highlighting sudden spikes in negative product sentiment.
Project Gallery & Mockups
The Business Impact
Surfaced critical usability issues within minutes of releases rather than days. Product managers can now gather actionable insights 4 times faster than they could with manual auditing, increasing average store ratings from 4.1 to 4.5.
Project Details
Mobile App Studio
3 Months
ML Engineers & Full Stack Dev