TextLens vs natural

TextLens is purpose-built for readability scoring and content analysis. natural is a Swiss army knife for NLP. Different tools for different jobs — here's how they compare.

$ npm install textlens

Side-by-side comparison

Feature	TextLens	natural
Focus	Readability + content analysis	General-purpose NLP
Dependencies	Zero	Multiple sub-packages
TypeScript	Native	— (JS only, @types available)
Readability formulas	8 formulas + consensus grade	—
Sentiment analysis	✓	✓
Keyword extraction	✓ TF-IDF	✓ TF-IDF
SEO scoring	✓	—
Reading time	✓	—
Tokenization	Basic (sentences, words)	✓ Multiple tokenizers
Stemming	—	✓ Porter, Lancaster
Classification	—	✓ Naive Bayes, logistic regression
Phonetics	—	✓ Soundex, Metaphone
String distance	—	✓ Levenshtein, Jaro-Winkler
Browser support	✓	Limited
CLI	✓	—
GitHub stars	New	10,000+
License	MIT	MIT

What TextLens has that natural doesn't

TextLens ships with 8 readability formulas, SEO scoring, and reading time estimation. natural has none of these.

Flesch-Kincaid Grade Level

Maps text to a U.S. school grade level based on sentence length and syllable count. The most widely used readability formula.

Gunning Fog Index

Estimates years of formal education needed. Penalizes complex (3+ syllable) words more heavily.

Coleman-Liau Index

Character-based formula. More reliable for technical text with acronyms and abbreviations.

SMOG Index

The gold standard for health literacy. Counts polysyllabic words to estimate reading difficulty.

Automated Readability Index

Character-based formula designed for automated assessment. Fast and effective for large documents.

Dale-Chall Readability

Compares words against a 3,000-word familiar list. Accurate for lower reading levels.

Spache Readability

Designed for primary-grade text (grades 1–3). Uses a familiar word list tuned for early readers.

Flesch Reading Ease

Scores text 0–100 (higher = easier). The most intuitive readability metric for non-technical users.

Code comparison

TextLens gives you readability scores in one call. natural requires separate modules for each NLP task.

TextLens TypeScript

import { analyze } from 'textlens';

const result = analyze(text);

result.readability.fleschKincaidGrade;  // 8.2
result.readability.gunningFogIndex;     // 10.1
result.readability.consensusGrade;      // 8.7
result.sentiment.label;                 // 'positive'
result.keywords;                        // ['readability', ...]
result.seo.score;                       // 82

natural JavaScript

const natural = require('natural');

const tokenizer = new natural.WordTokenizer();
tokenizer.tokenize(text);              // ['word', ...]

const Analyzer = natural.SentimentAnalyzer;
const stemmer = natural.PorterStemmer;
const analyzer = new Analyzer('English', stemmer, 'afinn');
analyzer.getSentiment(tokens);          // 0.25

// no readability formulas
// no SEO scoring
// no reading time

When to use each

Use TextLens for readability scoring and content analysis Blog post readability, documentation quality, SEO optimization, content grading. TextLens gives you 8 readability formulas, sentiment, keywords, and SEO scoring in a single analyze() call with zero dependencies.

Use TextLens for browser-side analysis TextLens runs in the browser with no server required. Build client-side writing tools, CMS plugins, or content checkers. natural's large dependency tree makes it impractical for browser use.

Use natural for NLP tasks Tokenization, stemming, classification, phonetics, string distance. natural covers a broad range of NLP operations that TextLens doesn't attempt. If you need a Naive Bayes classifier or Porter stemmer, natural is the right tool.

Use both together The two libraries complement each other. Use TextLens for readability and content scoring, natural for tokenization and classification. Zero dependency overlap means no conflicts.

Get started

$ npm install textlens

TextLens docs npm GitHub