AI-Powered Customer Sentiment Analysis
Led the development of a sentiment analysis tool that processes customer feedback across multiple channels to identify trends and improvement opportunities.
An enterprise-grade sentiment analysis system that processes customer feedback from support tickets, social media, and surveys to surface actionable insights in real time.
The Challenge
Our client needed to understand customer sentiment across thousands of daily feedback touchpoints — support tickets, social media mentions, survey responses, and app store reviews. Manual analysis couldn’t keep up, and simple keyword-based tools missed context and nuance.
What We Built
A custom NLP pipeline using domain adaptation techniques, handling multiple languages with industry-specific terminology. The system processes feedback in real time and surfaces trends on an analytics dashboard.
Architecture
The system follows a microservices-based architecture:
- Data Ingestion Services — Connectors for support platforms, social media APIs, survey tools, and app store feeds
- NLP Processing Pipeline — Transformer-based models fine-tuned on domain-specific data for accurate sentiment classification
- Real-Time Analytics Dashboard — React-based dashboard with live sentiment trends, drill-down capabilities, and alerting
- Automated Reporting & Alerting — Scheduled reports and real-time alerts when sentiment drops below thresholds
Tech Stack
- AI/ML: Python, TensorFlow, custom transformer models
- Backend: .NET Core API services
- Frontend: React with real-time data visualization
- Infrastructure: Azure, Docker, Kubernetes
- Data: Azure Event Hubs for streaming, Azure SQL + Cosmos DB for storage
Results
- 35% faster response to emerging customer issues
- Identified product improvements that were previously hidden in unstructured feedback
- Better allocation of support resources based on sentiment trends and predicted escalation
- Processing 10,000+ feedback items daily across 5 languages