Stock Sentiment Analyzer - Fintech Project

Project Overview

Developed a comprehensive financial sentiment analysis system that processes real-time news data to provide accurate market sentiment predictions. The system combines traditional NLP techniques with modern transformer models and Retriever-Augmented Generation (RAG) to minimize hallucinations and improve accuracy.

Challenge

Unstructured financial news led to low signal accuracy in traditional sentiment analysis, with high rates of false positives and model hallucinations affecting trading decision reliability.

Solution

Architected a comprehensive NLP pipeline combining spaCy for preprocessing, FinBERT for domain-specific sentiment analysis, and RAG with Pinecone for contextual accuracy. Implemented FastAPI microservices with Docker containerization for production deployment.

Results & Impact

• Achieved 89% sentiment prediction accuracy on financial news dataset
• Reduced model hallucinations by 30% through RAG implementation
• Handles 5,000+ requests per day with sub-200ms response times
• Deployed scalable API services supporting concurrent user sessions

Technologies Used