Sentiment Analysis Engine
The Problem
Every day, millions of people share opinions online — about products, politics, experiences. Companies spend billions trying to understand this firehose of unstructured text. Traditional keyword-based approaches miss sarcasm, context, and nuance. A tweet saying "Oh great, another update that breaks everything" reads as positive if you just count "great."
The challenge: Build a classifier that understands what people actually mean, not just what they literally say.
The Approach
- Data Collection: Large-scale social media dataset with diverse opinion types, sarcasm, and mixed sentiment
- Preprocessing: Custom tokenization pipeline handling hashtags, mentions, emojis, and slang normalization
- Model Architecture: Fine-tuned transformer-based architecture that captures long-range dependencies and contextual meaning
- Evaluation: Rigorous train/val/test split with confusion matrix analysis, per-class precision/recall, and error analysis on failure cases
Key Results
- 95% classification accuracy on held-out test set, significantly outperforming bag-of-words baselines
- Strong performance on sarcasm and mixed-sentiment cases where traditional methods fail
- Confusion matrix analysis revealed most errors occur at the positive/neutral boundary — a known hard problem
- Model handles out-of-vocabulary slang and new expressions through subword tokenization
Business Value
Brand Monitoring: Companies like Sprout Social and Brandwatch charge $1K+/month for sentiment analysis. This project implements the same core capability from scratch. Customer Feedback: Automatically routing negative sentiment to support teams reduces churn. Market Intelligence: Real-time sentiment on product launches enables rapid iteration.