Keyword Density Checker

Keyword density is the percentage of times a keyword appears in a text relative to the total word count. Formula: (keyword count / total words) × 100. A page about "blue widgets" with the phrase appearing 5 times in a 500-word article has 1% keyword density. Search engines use keyword frequency as one signal for topical relevance — but they also look at semantically related terms, not just exact matches.

There is no single "ideal" density. Historically, 1–3% was cited as a guideline, but modern SEO focuses on natural language and semantic relevance rather than hitting a specific percentage. Google's algorithms (including BERT and MUM) understand context and meaning, not just keyword frequency. Keyword stuffing (unnatural repetition to inflate density) is penalized by Google's spam policies. Focus on covering the topic comprehensively rather than hitting a target density.

Keyword stuffing is the practice of overloading web content with keywords in an attempt to manipulate search rankings. Examples: "We sell blue widgets. Our blue widgets are the best blue widgets. Buy blue widgets today." Google's Spam Policies explicitly prohibit stuffing keywords into content in ways that are unnatural. It results in penalties or manual actions. Modern SEO uses LSI (Latent Semantic Indexing) keywords and natural language variations instead.

For most SEO keyword density analysis, yes — stop words (the, a, is, of, in, etc.) should be excluded because they appear in all text and skew the density calculation. When analyzing for specific keyword phrases, include all words in the phrase. When analyzing single keywords, exclude stop words to focus on meaningful terms. Some tools offer both views. Google ignores most stop words in ranking calculations unless they change meaning (e.g., "to be or not to be").

TF-IDF (Term Frequency-Inverse Document Frequency) is more sophisticated: TF × IDF, where TF = term frequency in the document and IDF = inverse of how common the term is across all documents (log(total docs / docs with term)). Common words have low IDF; rare, specific words have high IDF. TF-IDF gives high scores to terms that appear frequently in a specific document but rarely across the web — indicating topic specificity. Modern search engines use variants of TF-IDF as ranking signals.