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Analyze text for word count, readability, and language stats. Paste and get instant insights.
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Comprehensive text analyzer computing 15+ statistics: characters (with/without spaces), words, sentences, paragraphs, lines, syllables, and average word/sentence length. Calculates readability scores (Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau), performs sentiment analysis (positive/negative/neutral), language detection, emotion detection (anger/joy/sadness/fear/surprise), named entity recognition (persons, organizations, locations, dates), vocabulary analysis (unique words, lexical density), and provides writing suggestions.
Manually computing readability, sentiment, and language metrics is impractical. This tool aggregates dozens of text analytics into one interface so writers and editors can assess tone, complexity, and quality instantly.
This tool is part of the DevStackIO platform — a collection of free online developer tools from DevStackIO. Browse more free developer resources on DevStackIO.
Content writers, editors, SEO analysts, linguists, students, and anyone who needs quantitative and qualitative analysis of written text.
Paste or type text into the input area; statistics update in real time as you type.
Scroll through the Stats section to view character, word, sentence, paragraph, and line counts.
Read the Readability section to see scores for Flesch-Kincaid, Gunning Fog, SMOG, and Coleman-Liau — each with a grade-level interpretation.
Review the Sentiment section showing positive/negative/neutral percentages and an overall sentiment label.
Check Language Detection for the detected language and confidence score.
Explore the Entities tab to see extracted persons, organizations, locations, and dates with their frequency.
Input: "The quick brown fox jumps over the lazy dog. It was a sunny day in the forest." → Stats: 14 words, 2 sentences, 68 chars; Flesch-Kincaid: 5.2 grade; Sentiment: neutral; Language: English; Entities: none detectedInput: "Apple Inc. announced on January 15, 2025 that Tim Cook would visit Beijing. This delighted investors." → Entities: [Apple Inc. (ORG), Tim Cook (PERSON), Beijing (LOC), January 15, 2025 (DATE)]; Sentiment: positive; Emotion: joy detectedHow is the Flesch-Kincaid score calculated?
206.835 - 1.015 × (words/sentences) - 84.6 × (syllables/words). Scores 90-100 = very easy, 0-30 = very difficult.
What languages does language detection support?
Over 50 languages including English, Spanish, French, German, Chinese, Arabic, and Russian, using a neural language identification model.
Can I analyze a PDF or document file? Paste the extracted text
file upload with text extraction is on the roadmap.
Are entity recognitions stored or sent to a server?
All processing is done client-side in the browser; no data is transmitted externally.
Authoritative specifications, standards, and in-depth reading for Text Analyzer.
Enter text for comprehensive analysis including sentiment, language detection, and readability
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