Keyword Density Checker
Analyze keyword frequency and density in articles and blog posts. Identifies repeated terms, 1-word and 2-word phrases, and prevents keyword stuffing.
Difficult (College Student)
Estimated U.S. School Education Level
Based on character counts per 100 words
Top Keyword Frequency & Density
Step-by-Step Instructions
Paste Article Text or Web Page Content
Paste article copy, blog drafts, or marketing content into the text analyzer.
Configure Stop Word Filters
Toggle exclusion of common English stop words (the, and, is, in, at, of) to isolate meaningful keyword phrases.
Analyze 1-Word, 2-Word, and 3-Word N-Grams
Review frequency tables and percentage density for single keywords and multi-word phrases.
Check for Keyword Stuffing Warnings
Verify that primary keyword densities remain in the safe SEO target range (1.0% to 2.5%).
Key Features & Capabilities
- Keyword Density formula: $\text{Density \%} = \left(\frac{\text{Keyword Count}}{\text{Total Words}}\right) \times 100\%$
- N-Gram text phrase analyzer parsing 1-word (Unigrams), 2-word (Bigrams), and 3-word (Trigrams) phrases
- Keyword Stuffing alert system flagging phrases exceeding 3.0% density to protect against Google penalties
- Total word count, unique word count, lexical density, and reading time metrics
Frequently Asked Questions
What is the ideal keyword density for SEO in modern search algorithms?
Modern SEO best practices recommend a target keyword density between **1.0% and 2.0%** for your primary target keyword (roughly 1 to 2 mentions per 100 words of text). Going above **3.0%** triggers algorithmic keyword stuffing penalties under Google's helpful content systems.
How does Google evaluate content relevance beyond raw keyword density?
Google uses advanced NLP models (BERT, RankBrain, Gemini) that analyze **Latent Semantic Indexing (LSI)** and semantic entities. Search engines look for related contextual synonyms, topical depth, and comprehensive coverage of sub-topics rather than repeated exact-match keyword repetitions.
What is an N-Gram in keyword density analysis?
An N-gram is a contiguous sequence of *n* words from a given text. **Unigrams (1-word)** identify single keywords; **Bigrams (2-word)** identify two-word phrases (e.g., 'credit card'); **Trigrams (3-word)** identify long-tail search intent phrases (e.g., 'best credit card').
What are Stop Words and why should they be filtered during keyword analysis?
Stop words are high-frequency grammatical words ('the', 'is', 'at', 'which', 'on', 'and') that carry minimal semantic meaning. Filtering stop words ensures your keyword density report focuses strictly on substantive, search-relevant vocabulary.
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