🧠 Information Retrieval & Text Mining: Natural Language Processing Matrices and TF-IDF Weight Optimizations
This tool tokenizes pasted text in your browser and ranks words by frequency and a single-document prominence score (TF combined with a log-style rarity boost). It is not connected to Google, does not use a multi-page corpus, and does not perform true named-entity recognition.
Use it to spot repeated phrases, check whether a focus topic appears often enough, and avoid obvious keyword-stuffing patterns before publishing.
What the scorecard shows
- Raw count: How many times a token appears (after stop-word removal).
- Prominence: Relative weight within this document — higher for frequent but not ubiquitous terms.
- Density %: Share of all counted tokens; above 2.5% is flagged as possible stuffing.
Preventing Over-Optimization Penalties and Algorithmic Throttling
When writers attempt to artificially force search relevance by manually repeating the same keyword strings throughout an article, modern search engine filters immediately flag the text for over-optimization. This triggers an automated structural penalty that suppresses your page's organic visibility and drops its impression metrics across search results page lines.
The table highlights up to 25 terms. Optional focus topic words are marked when they match your input.
Writing tips
Cover related vocabulary: Use synonyms and related concepts instead of repeating one exact phrase.
Check the intro: Important topic words should appear early, but natural readability matters more than raw counts.
FAQ
What is a semantic entity, and how does it differ from a basic keyword?
A keyword is merely a single, isolated string of characters text searchers type into a query line. A semantic entity is a clearly defined, real-world concept, noun, or subject mapped into a database graph (like Google's Knowledge Graph). Modern search bots evaluate content by mapping how your page connects these entities, rather than simply counting individual repeating words.
How do stop-words filters protect the accuracy of my density scores?
If the parsing loop counted every single word indiscriminately, common grammatical fillers like 'the' or 'and' would flood the scorecard, drowning out your true context. Automatically filtering out these stop-words allows our TF-IDF math matrix to focus purely on the specialized terms that dictate your site's true topical authority.
What is an optimal TF-IDF weight score for a competitive technical article?
There is no single magic number, as score weights scale relative to the overall length of your text copy document. The best approach is to check that your primary target keywords sit comfortably at the top of the chart with the highest relative scores, while keeping their individual density percentages safely below the 2.5% over-optimization threshold.
Is my text sent to your server?
No. Tokenization and scoring run entirely in JavaScript in your browser. Only the normal page HTML is loaded from the server.