NLP Sentiment Analysis with Transformers

Project Overview

This project implements a sophisticated sentiment analysis system using transformer-based models to accurately classify text sentiment. The system goes beyond basic positive/negative classification by detecting nuanced emotional tones and providing confidence scores.

Key Features

• Fine-grained Sentiment Detection: 5-point scale sentiment classification (very negative to very positive)
• Multi-domain Adaptation: Model fine-tuning for specific domains (social media, product reviews, news)
• Low-latency Inference: Optimized for real-time analysis with batch processing capabilities
• Explainable Results: Visualization of which words and phrases influenced the sentiment prediction
• Multilingual Support: Extended functionality for analyzing text in multiple languages

Technologies Used

• PyTorch: Deep learning framework for model development
• Transformers (Hugging Face): BERT and RoBERTa models for NLP
• ONNX Runtime: For model optimization and deployment
• FastAPI: Backend API development
• Streamlit: Interactive demo application

Results

The final system achieves 92.3% accuracy on benchmark sentiment datasets while maintaining sub-50ms inference times for typical text inputs. The model demonstrates strong cross-domain performance and provides interpretable explanations for its predictions.