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Text Statistics Analyzer Examples
The Text Statistics Analyzer provides comprehensive metrics about text content including word count, character count, readability scores, and keyword frequency. Below are practical examples showing what the analyzer measures and how to interpret the results.
Basic Count Metrics
// Sample text:
"The quick brown fox jumps over the lazy dog.
Pack my box with five dozen liquor jugs."
// Statistics output:
Words (total): 16
Words (unique): 15
Characters (with spaces): 87
Characters (no spaces): 72
Characters (no punctuation): 70
Sentences: 2
Paragraphs: 1
Lines: 2
Average word length: 4.5 characters
Average sentence length: 8 words
Reading Time Estimation
// Reading speed assumptions:
// Average adult: 200-250 words per minute (wpm)
// Technical content: 150-200 wpm
// Skimming: 400-700 wpm
// Example calculations:
500 words → ~2 min read (at 250 wpm)
1000 words → ~4 min read (at 250 wpm)
2000 words → ~8 min read (at 250 wpm)
5000 words → ~20 min read (at 250 wpm)
// Blog post benchmark:
// Ideal length: 1,500–2,500 words (6–10 min read)
// Long-form: 3,000+ words (12+ min read)
Readability Scores Explained
// Flesch Reading Ease (0-100, higher = easier)
90-100: Very easy (5th grade)
80-90: Easy (6th grade)
70-80: Fairly easy (7th grade)
60-70: Standard (8th-9th grade)
50-60: Fairly difficult (10th-12th grade)
30-50: Difficult (college level)
0-30: Very difficult (professional/academic)
// Formula:
// 206.835 - (1.015 × avg words/sentence) - (84.6 × avg syllables/word)
// Example scores:
"See Spot run." → ~100 (very easy)
"The cat sat on the mat." → ~90 (easy)
"Configure the database settings." → ~60 (standard)
"Implement polymorphic dispatch." → ~30 (difficult)
Flesch-Kincaid Grade Level
// Grade level = US school grade required to understand the text
// Formula: 0.39 × (words/sentences) + 11.8 × (syllables/words) - 15.59
// Target grade levels by content type:
// General web content: Grade 6-8
// News articles: Grade 8-10
// Business writing: Grade 10-12
// Academic papers: Grade 12+
// Legal documents: Grade 14+
// Example:
Text: "The server processes requests asynchronously using event-driven architecture."
Grade level: ~14 (college/professional)
Text: "The server handles requests one at a time as they come in."
Grade level: ~8 (middle school)
Keyword Frequency Analysis
// Sample text (100 words about web development):
"Web development involves building websites and web applications.
Frontend development uses HTML, CSS, and JavaScript. Backend
development uses languages like Python, Java, and Node.js.
Full-stack developers work on both frontend and backend.
Modern web development uses frameworks like React and Vue
for frontend, and Express or Django for backend services."
// Top keywords (stop words removed):
development → 5 occurrences (5%)
web → 4 occurrences (4%)
frontend → 3 occurrences (3%)
backend → 3 occurrences (3%)
uses → 3 occurrences (3%)
frameworks → 1 occurrence (1%)
Sentence Length Analysis
// Sample text analysis:
Sentence 1: "The quick brown fox jumps over the lazy dog." → 9 words
Sentence 2: "It was a dark and stormy night." → 8 words
Sentence 3: "The implementation of the asynchronous event-driven
architecture requires careful consideration of the
threading model and memory management strategies." → 22 words
// Statistics:
Shortest sentence: 8 words
Longest sentence: 22 words
Average: 13 words
Sentences over 20 words: 1 (flagged for review)
// Readability guideline:
// Aim for average sentence length of 15-20 words
// Flag sentences over 30 words for simplification
Vocabulary Richness
// Type-Token Ratio (TTR) = unique words / total words
// Higher TTR = more varied vocabulary
// Example 1 (repetitive text):
"The cat sat on the mat. The cat ate the rat."
Total words: 12, Unique words: 7
TTR: 7/12 = 0.58 (moderate)
// Example 2 (varied vocabulary):
"The feline rested upon the carpet. The tabby consumed the rodent."
Total words: 12, Unique words: 11
TTR: 11/12 = 0.92 (high variety)
// Benchmark:
// TTR > 0.7: good vocabulary variety
// TTR < 0.5: potentially repetitive content
Paragraph Structure Analysis
// Sample blog post analysis:
Paragraph 1: 45 words (intro)
Paragraph 2: 120 words (main point 1)
Paragraph 3: 95 words (main point 2)
Paragraph 4: 180 words (main point 3) ← flagged: too long
Paragraph 5: 38 words (conclusion)
// Statistics:
Total paragraphs: 5
Average paragraph length: 95.6 words
Longest paragraph: 180 words (flagged)
Shortest paragraph: 38 words
// Guideline: Keep paragraphs under 150 words for web content
Content Optimization Targets
// Blog post targets:
Word count: 1,500–2,500 words
Reading time: 6–10 minutes
Flesch Reading Ease: 60–70 (standard)
Grade level: 8–10
Avg sentence length: 15–20 words
Avg paragraph length: 50–100 words
// Technical documentation targets:
Word count: 500–2,000 words per page
Flesch Reading Ease: 50–60 (fairly difficult)
Grade level: 10–12
Avg sentence length: 15–25 words
// Email newsletter targets:
Word count: 200–500 words
Reading time: 1–2 minutes
Flesch Reading Ease: 65–75 (fairly easy)
Grade level: 7–9
Character Count for Platform Limits
// Platform character limits:
Twitter/X post: 280 characters
LinkedIn post: 3,000 characters
Instagram caption: 2,200 characters
Meta description: 155–160 characters
Title tag: 50–60 characters
SMS message: 160 characters (1 segment)
YouTube description: 5,000 characters
// Check your text against these limits using the analyzer
Common Use Cases
- Verify blog posts meet minimum word count for SEO
- Check readability score before publishing content
- Identify overly long sentences for simplification
- Analyze keyword density for SEO optimization
- Ensure content fits within platform character limits
- Estimate reading time for article metadata
- Compare vocabulary richness across document versions
- Verify documentation meets style guide requirements
Paste your text into the Text Statistics Analyzer and get a complete metrics report including word count, readability scores, keyword frequency, and reading time estimate.