Keyword Extractor

How to Use

Paste any block of text, an article, a competitor's page, your own draft, and this tool pulls out the words and short phrases that appear most often, automatically filtering out common words like "the" and "and" that don't carry topical meaning. Copy the result as a ready-to-use comma-separated list. Toggle "Include two-word phrases" off if you only want single-word results, useful when you're specifically hunting for the dominant topic words rather than exact recurring phrases.

Extraction vs Density: Two Different Questions

It helps to think of this tool and the Keyword Density Checker as answering two different questions about the same text. Density Checker answers "exactly how often, and what percentage, does this specific term appear?", it's built for auditing one term at a time with precise numbers. Keyword Extractor answers a broader question: "what are the handful of terms and phrases this text is actually about?", it's built for scanning quickly and walking away with a usable list, not a percentage table. If you already know the exact term you're checking, use Density Checker. If you're trying to discover what a piece of text emphasizes without reading the whole thing, Extractor is the faster tool.

Worked Example: Summarizing an Agency's Homepage Copy

Take this sample paragraph: "Our digital marketing agency helps small businesses grow through digital marketing strategy, social media marketing, and search engine optimization. A strong digital marketing plan combines content, social media, and SEO to drive real business growth for small businesses." Running it through this tool with phrases included and Top 20 selected surfaces "marketing" (4), "digital" (3) and "digital marketing" (3) as the clear leaders, followed by "small," "businesses," "social," "media," "small businesses," and "social media" each appearing twice. Without reading the paragraph in full, that chip list alone tells you accurately that this is agency copy about digital marketing services aimed at small businesses, with social media as a secondary emphasis, exactly the kind of fast topical read this tool is built to produce.

A Practical Use Case: Building a Content Brief

A common workflow is pasting a handful of top-ranking competitor pages for a target topic into this tool one at a time, noting which words and phrases repeat across most or all of them. Terms that show up consistently across several competing pages are usually terms searchers expect any thorough page on the topic to cover, they make a solid checklist for your own content brief. Terms that appear in only one competitor's page but not the others might be a differentiator worth adopting, or might be that one page's unique framing, worth a second look either way rather than automatically copying it in.

Limitations Worth Knowing

This tool works purely on literal text repetition, it has no awareness of grammar, part of speech, or meaning. It will not merge "run," "running," and "ran" into one concept, and it treats a two-word phrase spanning a sentence boundary (the last word of one sentence followed by the first word of the next) the same as a phrase that's actually one continuous idea, since it only looks at adjacent words in the raw text stream, not sentence structure. For most SEO and content-planning use cases this is a reasonable tradeoff for speed, but for detailed linguistic analysis of a manuscript or transcript, a dedicated natural-language-processing tool would give a more precise breakdown.

Choosing How Many Keywords to Extract

The Top 10/20/30 setting controls how deep the list goes, not how the ranking works, results are always sorted by frequency first regardless of which cutoff you choose. Top 10 is usually enough for a short blog post or product description, where only a handful of terms genuinely repeat. Longer documents, full articles, transcripts, or several pages of combined competitor copy, tend to benefit from Top 30, since a shorter cutoff can quietly drop secondary themes that still repeat often enough to be meaningful, just not as often as the single dominant topic.

Frequently Asked Questions

How is this different from the Keyword Density Checker?

Density Checker shows an exact frequency table with percentages for one phrase length at a time. Extractor is built to quickly pull out a clean, copyable list of the most meaningful single words and two-word phrases together, useful for content briefs, tags, or a quick topical summary.

Can I use the extracted list as my meta keywords tag?

You can, though keep in mind major search engines no longer use the meta keywords tag for ranking. The extracted list is more useful as a quick content summary, for generating tags, or for spotting what topics a piece of text actually emphasizes.

Why does a word need to appear more than once to show up in the results?

The tool only surfaces words and phrases that repeat at least twice, since a term that appears only once usually isn't a meaningful theme, it's likely an incidental mention. This filtering keeps the extracted list focused on genuinely recurring topics instead of listing every distinct word in the text, which for a long article could otherwise produce hundreds of one-off entries.

What can I use an extracted keyword list for?

Common uses include building a quick content brief before writing (what terms should a competing or companion article realistically cover), auditing your own draft to confirm it emphasizes the topics you intended, generating tag suggestions for a CMS or blog platform, and getting a fast topical summary of a long document without reading it in full.

Does this tool understand word meaning, or just count repetition?

It counts repetition, it does not understand meaning, synonyms, or context the way a large language model or a modern search engine's ranking system does. It won't group "running" and "run" together, or recognize that "car" and "automobile" refer to the same concept. Treat the output as a frequency-based starting point for a human review, not a finished semantic analysis.

Why do some two-word phrases in the list overlap with single words already shown?

This is expected and intentional. Single words and two-word phrases are counted independently, so a heavily repeated phrase like "digital marketing" can appear as its own chip while "digital" and "marketing" also appear separately as individual words, since each also occurs on its own elsewhere in the text or as part of other phrases. Seeing both levels together is more informative than picking just one, it shows both the overall theme words and the specific phrasing used to express them.