# Nlp Basics

> Apply foundational NLP for analytics — text cleaning, representation, classification, and topic patterns — with awareness of limits of language models. Use when analyzing text data such as reviews, tickets, surveys, or documents.

- Skill: `itsual/nlp-basics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add itsual/nlp-basics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/itsual/nlp-basics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: itsual (https://skillmd.com/u/itsual)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/itsual/nlp-basics

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# 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