Entity Density Analyzer
Modern SEO and GEO are about entities, not just keywords. Search engines and AI Overviews reward pages with clear, well-distributed named entities that reinforce topical authority. Our analyzer extracts proper nouns and 2-gram phrases from your page, ranks them by frequency, and shows density percentages so you can spot thin coverage or topic dilution.
How it works
A short, focused workflow — input, run, read the result.
Enter a page URL
Paste any public article URL. The tool fetches the HTML, strips non-content blocks, and tokenizes the text.
Review the entity list
Detected 1-gram and 2-gram entities are ranked by frequency with density percentages. Use the filter to focus on specific terms.
Adjust content for entity balance
A healthy page has a strong target entity (top of the list with 5+ mentions) plus 4-8 supporting entities that reinforce the topic.
What is entity density, and why does it shape topical authority?
An entity is a named person, brand, product, place, or concept. Modern SEO and GEO are scored on entities, not just keywords. A page with weak entity distribution tells Google and AI Overviews it doesn't really know the topic.
What this entity density analyzer does
Every signal that decides whether Google and AI engines see your page as a topical authority.
- Named entity extraction — detects proper nouns for people (Tim Cook), brands (Apple), products (iPhone), and places (Paris) using capitalization heuristics.
- 2-gram phrase detection — captures compound named concepts like 'Knowledge Graph' or 'AI Overview' that single-token extraction misses.
- Density percentage — shows each entity's share of total words so you can spot under- and over-represented topics at a glance.
- Frequency ranking — orders all detected entities by count so the dominant entity and the long tail are visible in one scroll.
- Topical focus check — flags pages where the dominant entity falls below 1% density, which signals weak topical anchoring.
- Topic dilution check — flags pages with 30+ entities appearing only once, which signals scattered focus and no clear topic.
- Filter by term — free-text search to isolate a specific entity across the page and see every mention in context.
- Stop word exclusion — removes noise words (The, This, And) so the entity list reflects meaningful concepts, not grammar.
Who uses this entity density analyzer
SEO and content teams who want pages to rank for entities, not just keyword strings.
You're auditing 200 blog posts and need to know which pages have weak entity coverage before adding internal links.
Spot the thin pages in 5 minutes per URL, prioritize the ones missing 3+ supporting entities.
You wrote a 2,000-word article and want to confirm it covers the entities a top-ranking competitor does.
Compare your entity list against the expected profile, then add the missing concepts in the next edit.
You deliver 30 client articles a month and need a quality gate for entity coverage before publish.
Run every draft through the analyzer, reject any post under 5 supporting entities, fix the gaps.
You're investigating why a page ranks position 7 despite solid backlinks and good content.
Find the missing entities the page should mention, add 3-5, watch the page move to position 3-4.
You publish 2 posts a week on a niche topic and want each post to reinforce your domain's topical authority.
Track your entity distribution over time, see authority compound as the cluster deepens.
You're generating 50 GEO-optimized articles and need a way to verify entity coverage isn't drifting.
Run every AI-generated draft through the analyzer, catch the entity gaps before the post goes live.
Related glossary terms
Want a deeper dive? These glossary entries explain the concepts behind this tool.
Frequently Asked
Questions
Everything you need to know about entity density analysis for SEO in 2026.
An entity is a specific, named thing: a person (Tim Cook), place (Paris), brand (Apple), product (iPhone), or concept (Generative Engine Optimization). Google's Knowledge Graph and AI Overviews work with entities, not just strings of text. Pages that clearly identify their entities tend to rank better for entity-driven queries.
Keyword density counts literal word repetitions (often 1-gram, case-insensitive). Entity density counts named, capitalized concepts that carry semantic meaning. Two pages can have identical keyword density but very different entity profiles. Entity coverage is what AI engines and Knowledge Graph weight. Keyword count alone is a legacy metric.
A page targeting one topic should have: 1 dominant entity (5+ mentions, 1-2% density), 4-8 supporting entities (2-4 mentions each, 0.3-0.8% density), and a long tail of incidental entities (1 mention). If only 1-2 entities appear, the content is too narrow. If 30+ entities all appear once, the focus is too diffuse.
The detector uses capitalization heuristics, so lowercase brands or non-English text may be missed. It also excludes common stop words (The, This, And) to reduce noise. For deeper analysis with context-aware extraction, an LLM-based tool would be more thorough, but cost more.
Yes. The page is fetched once via our server, content is extracted, entities are counted, then the response is returned. We do not log URL contents, do not store page text, and do not use it for training. Rate limit is 5 checks/hour per IP.
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