* Note: Built-in stop-word filtering is optimized for English vocabulary. Total word count calculations include all tokens.
Keyword density measures the percentage of times a specific target word or phrase appears relative to the total word count of a document:
$$\text{Density (\%)} = \left( \frac{\text{Keyword Frequency} \times \text{Words in Keyword}}{\text{Total Word Count}} \right) \times 100$$
Modern search algorithms evaluate topical depth using natural language processing (NLP) and Latent Semantic Indexing (LSI). Analyzing 2-word (bigram) and 3-word (trigram) phrases provides insights into natural language flow, ensuring secondary supporting concepts (e.g. 'search engine optimization', 'rich snippet results') are well-represented alongside primary head terms.
Scenario: Analyzing target keyword distribution for an SEO guide.
500-word article mentioning 'web performance' 8 times and 'optimization' 10 times
'web performance': 8 occurrences (3.2% 2-word density) | 'optimization': 10 occurrences (2.0% 1-word density)
Confirms balanced, natural keyword distribution within recommended thresholds.
Scenario: Reviewing an affiliate review page with repetitive brand mentions.
400-word product review mentioning brand name 28 times
Density: 7.0% (Warning: Keyword Stuffing detected)
Alerts the writer to replace repetitive brand mentions with natural synonyms.
Simulate how your optimized title and description look on Google.
Generate title tags and descriptions using your top analyzed keywords.
Add structured data to support your optimized content topics.