Building a Sentiment Analysis System with NLP

# Building a Sentiment Analysis System with NLP Sentiment analysis is one of the most practical applications of NLP. In this guide, I'll share how I built a sentiment analysis system achieving 89% accuracy using transformer models. ## Understanding Sentiment Analysis Sentiment analysis classifies text as positive, negative, or neutral. Modern approaches use transformer models like BERT and RoBERTa for nuanced understanding. ## Architecture ### Model Selection I experimented with several models: - **BERT-base**: Good baseline, 86% accuracy - **RoBERTa-large**: Best accuracy, 89% - **DistilBERT**: Fast inference, 84% accuracy ### Fine-tuning Strategy Pre-trained models need fine-tuning on domain-specific data. Key considerations: 1. Learning rate: 2e-5 (lower than pre-training) 2. Epochs: 3-5 (avoid overfitting) 3. Warmup steps: 10% of total steps ## Data Preparation ### Dataset Combined multiple sources: - Product reviews - Social media posts - Movie reviews ### Preprocessing 1. Clean text (remove HTML, special characters) 2. Handle emojis (convert to text descriptions) 3. Balance classes 4. Train/validation/test split ## Implementation ### Training Loop Standard PyTorch training with: - Cross-entropy loss - AdamW optimizer - Linear learning rate decay ### Evaluation Metrics - Accuracy: 89% - F1-score: 0.87 - Precision: 0.88 - Recall: 0.86 ## Advanced Features ### Aspect-Based Sentiment Extract sentiment toward specific entities mentioned in text. ### Sarcasm Detection Additional classifier for sarcastic content to avoid misclassification. ### Multi-language Support Using mBERT for cross-lingual sentiment analysis. ## Deployment ### API Design FastAPI endpoint with: - Batch processing support - Confidence scores - Caching for common queries ### Performance - Latency: <100ms per request - Throughput: 100 requests/second ## Lessons Learned 1. Data quality beats model complexity 2. Domain-specific fine-tuning is essential 3. Handle edge cases (emojis, sarcasm) 4. Monitor model drift in production ## Conclusion Building sentiment analysis systems is accessible with modern NLP tools. Start with pre-trained models and focus on data quality for best results.