itsual NLP Basics (for Analytics)
Overview
NLP turns text into analyzable signal. For analytics work, practical pipelines and validation matter more than the newest model name.
When to Use
- Support ticket and review analysis
- Survey open-ends theme extraction
- Document classification and routing
- Lightweight information extraction
Core Practices
- Define the text task clearly (classify, extract, cluster, summarize)
- Clean and normalize with care for domain language
- Start with strong baselines before heavy models
- Evaluate with labeled samples and error analysis
- Watch for leakage and train/test contamination in text features
- Consider privacy when text contains personal data
Principles
- Domain jargon breaks generic tools
- Multilingual data needs explicit strategy
- LLM outputs need evaluation like any model
- Human-in-the-loop often required for high-stakes text decisions
Verification
- Task and success metrics are defined
- Evaluation uses realistic labeled examples
- Privacy constraints are respected