Related Keywords Extractor
Paste a chunk of content — a competitor's top-ranking article, your own draft, an authoritative reference page. The extractor surfaces the most semantically-loaded single words, bigrams, and trigrams that define the topic, with stopword filtering and frequency-vs-distinctness weighting. Use it to confirm topical coverage, find content gaps, or build out a related-keyword list before drafting.
Why Topical Coverage Beats Single-Keyword Targeting
Google has been semantic since the 2013 Hummingbird update and has only become more so: pages that cover a topic's full semantic neighborhood consistently outrank pages that hit one keyword hard but miss the surrounding terminology. Modern ranking factors in entity recognition, query-fan-out, and topical authority — meaning a page about "running shoes" that never mentions "midsole," "drop," "stability," or "neutral pronation" reads as thin even if it repeats "running shoes" twenty times. Extracting the related-keyword graph from a top-ranking competitor reveals what the SERP considers complete coverage.
How Extraction Works
The extractor tokenizes your content, lowercases, strips punctuation, and removes a 280-word English stopword list. It then computes raw frequency for unigrams, bigrams, and trigrams, weighted by a distinctness factor — terms that appear often but only in this content (high tf, low expected df) score higher than generic vocabulary. Output is grouped by n-gram length and sorted by composite score. For long content (over 5,000 words), the extractor samples paragraphs to keep processing under one second; for short content (under 200 words), it widens its phrase window to compensate for low signal.
Frequently Asked Questions
How does this differ from keyword density analysis?+
Density measures how often you used a keyword; this tool surfaces which keywords define the topic regardless of your usage. They are complementary — use the extractor on a competitor's content, then run
Keyword Density Checker on your own draft to see which surfaced terms you have covered.
Will the extracted keywords help my SEO directly?+
Yes — but as a coverage checklist, not a stuffing list. Use the output to confirm your draft addresses each major sub-topic, not to cram terms in unnaturally. Forced keyword insertion still hurts.
What length of content works best?+
Three hundred to three thousand words is the sweet spot. Below 300 the signal is too sparse; above 3,000 the noise dominates unless the content stays tightly on-topic. For very long pieces, paste sections individually.
Does it work for non-English content?+
Partial. The tokenizer and 280-word stopword list are English-specific. Non-English content extracts but with noise — common foreign-language function words appear in the output. Stopword expansion for other languages is planned.
Are bigrams or trigrams more useful?+
Depends on the topic. Technical fields tend to surface trigrams (client-side rendering performance); general topics surface bigrams (running form). Single words rarely tell the full story but are useful for confirming the core vocabulary is in your draft.
Can I paste a URL instead of content?+
No — fetching arbitrary URLs from the browser hits CORS restrictions and is not reliable. Copy-paste from the source page (Reader View handles paywall pages well) or use View Source for HTML.
Is my pasted content private?+
Yes — completely. Tokenization, stopword filtering, and scoring all happen locally in your browser. The pasted text is never transmitted to any server, never logged, and never used for analytics.
Will Google penalize me for using a related-keyword list?+
Not if the list informs natural writing. Google explicitly rewards comprehensive coverage; the penalty comes from forced insertion, not from knowing what to cover.
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