The DirJournal NLP Entity Gap Analyzer is a semantic SEO tool that compares two web pages to identify missing Named Entities and topical gaps in content.
Limited to 3 analyses per minute. Both pages fetched server-side, first ~6,000 characters each passed to Claude haiku 4.5 for NER.
Modern search relies on interconnected entities — people, organizations, technologies, places — not isolated keywords. A page that names the right entities in the right relationships establishes topical authority the way a single head term cannot.
Entity gap analysis makes that visible. The competitor names ten organizations on a topic and you name three? That gap is your content brief.
An NLP entity gap analysis extracts the Named Entities (people, organizations, locations, concepts) from two pages on the same topic, then computes the difference — which entities both pages cover, which entities the competitor covers that you don't, and which are unique to you. The missing column is the actionable list: it shows the topical authority signals a top-ranking page on your subject expects to see.
Modern ranking systems no longer match queries to keyword strings alone — they match queries to entities and the relationships between them. A page that covers all the entities a topic implies (the people, frameworks, tools, locations, methodologies tied to it) signals topical authority more strongly than a page that hits the head keyword in isolation. Closing entity gaps is how you give the model evidence that your page is genuinely about the subject, not just titled about it.