Analyze how often any keyword or phrase appears in your content. Get density percentage, TF score, bigram and trigram breakdowns, over-optimization alerts, and a sentence-level distribution map. The most complete free keyword density tool online.
Analyze Keyword Density
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Analysis Results
| # | Keyword | Count | Density | Status | Density Bar |
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| # | Bigram (2-word phrase) | Count | Density | Status |
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| # | Trigram (3-word phrase) | Count | Density | Status |
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What Is Keyword Density?
Keyword density is the percentage of times a specific keyword appears in a piece of text compared to the total word count. The formula is (keyword count / total words) x 100. For example, a 500-word article that uses "digital marketing" 10 times has a keyword density of 2 percent for that phrase.
Keyword density is one of the original on-page SEO signals. While Google has evolved well beyond simple frequency counting, density analysis remains a useful quality check. It helps writers confirm a target term appears with enough frequency to signal relevance, and helps editors catch keyword stuffing before it triggers spam filters.
What Is the Ideal Keyword Density for SEO?
Google has never published an official target range. Based on analysis of top-ranking content across many industries, most SEO practitioners treat the following as practical guidelines:
These are not hard limits. A technical reference page may naturally repeat a specific term at 3 percent without any stuffing. A product page with multiple product names might show 4 percent for the brand name. Always read the content aloud. If repetition feels unnatural, reduce it regardless of the percentage.
Google's position on keyword density: John Mueller confirmed in 2021 that Google does not use keyword density as a ranking signal. However, Google's spam classifier does penalize pages with unnatural keyword repetition. The goal of density analysis is not to hit a number but to catch unnatural patterns before publishing.
Why Bigrams and Trigrams Matter More Than Single Keywords
Modern search engines use semantic analysis that looks well beyond individual word frequency. Analyzing bigrams (two-word phrases) and trigrams (three-word phrases) reveals which multi-word combinations appear most prominently in your content. This is important because:
- Long-tail keywords are almost always bigrams or trigrams. A page optimized for "best keyword density checker free" will only look optimized for that phrase if the bigram and trigram analysis confirms those words cluster together with high frequency.
- High-frequency bigrams and trigrams show which concepts you are inadvertently emphasizing, which may or may not match your intended topic focus.
- Competitor content often wins on bigram and trigram matching even when single keyword density is similar, because multi-word phrase density is harder to manipulate and more semantically meaningful.
Stop Words and Why They Should Be Filtered
Stop words are high-frequency function words like the, a, is, and, of, in, to. Including them in density analysis produces misleading results because they dominate word counts without carrying any topical meaning.
This tool filters over 200 English stop words by default when generating the top keywords list. This means the frequency table shows only content words that carry actual semantic weight. Stop word filtering is disabled for exact-phrase matching so that phrases like "how to" or "what is" return accurate counts for the full phrase as written.
Keyword Density vs TF-IDF: Understanding the Difference
| Metric | What It Measures | Scope | SEO Use |
|---|---|---|---|
| Keyword Density | Frequency as % of total words in one document | Single document | Quick on-page check, stuffing detection |
| TF (Term Frequency) | Raw count or normalized frequency within a document | Single document | Core component of TF-IDF scoring |
| TF-IDF | Frequency weighted by rarity across a corpus of documents | Document vs. corpus | Topical relevance scoring, content gap analysis |
| Semantic Density | Distribution of related terms and concepts across content | Entity-level | Topical authority assessment |
This tool provides TF scores alongside raw frequency counts. For true TF-IDF analysis, a document corpus is required, which is not possible in a browser-only tool without access to a search index. The TF scores shown here indicate how prominently a term appears relative to the document itself, which is a useful proxy for understanding which terms dominate the content.
How to Use Keyword Density Checker for Better SEO
- Draft check: Paste your article draft before publishing. Confirm your primary keyword appears in the 1 to 2 percent range. Check that your secondary keywords appear at least 2 to 3 times each in a 1000-word piece.
- Competitor comparison: Paste a competitor's article and note which bigrams and trigrams dominate. Compare these to your own content to find phrase-level gaps.
- Over-optimization audit: If a page has dropped in rankings, paste its content and check for keywords above 3 percent. Reduce repetition by using synonyms and related terms.
- Content brief validation: Use the bigram and trigram tables to verify that multi-word target phrases appear with adequate frequency before sending content for publication.
- Anchor text audit: Paste all the anchor text from your backlink profile and check for over-optimized exact-match anchor patterns.
Distribution Map: Why Keyword Placement Matters
Keyword frequency tells you how often a term appears. Keyword distribution tells you where in the content it appears. Search engines weight keyword placement, giving more value to occurrences in titles, opening paragraphs, and headings. A keyword that appears 10 times but all in the final section of a long article gives weaker topical signals than the same keyword distributed evenly across the full piece.
The Distribution Map tab in this tool visualizes keyword placement across 10 equal segments of your content. A healthy pattern shows the target keyword present in most segments with slightly higher concentration in the first and last segments. A pattern that shows the keyword only in a few segments suggests the content could benefit from more even topical threading.
Frequently Asked Questions About Keyword Density
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