Every piece of text has a few words doing most of the work. ToolOrbit's Keyword Extractor finds them. Paste an article, a transcript, or a product description and instantly see the top keywords and two-word phrases ranked by how often they appear.
How the Keyword Extractor works
The tool is frequency-based, not AI. It does not infer meaning or search intent the way a language model might. It counts. After stripping out common stop words like 'the' and 'and,' it tallies the remaining single words and adjacent two-word phrases, then ranks them by count.
That two-word phrase, or bigram, ranking is useful because real topics are often pairs: 'climate change,' 'machine learning,' 'customer support.' Single-word counts alone miss these, so the tool surfaces both.
Finding the main keywords in a text
- Paste your text into the input area.
- Review the ranked list of top keywords and two-word phrases.
- Note the counts to see which terms dominate.
- Copy the keywords you want to reuse.
Practical uses for extracted keywords
- SEO audits: check whether a page actually emphasizes its target terms.
- Tagging and metadata: generate a starting list of tags for a blog post.
- Content gap checks: see if competitor text leans on terms you have ignored.
- Research: skim the dominant themes of a long document before reading it in full.
Tips for cleaner results
- Give it enough text. A few sentences will not produce meaningful frequency rankings.
- Expect raw counts. The tool does not judge relevance, so filter the list yourself.
- Combine with the Readability Checker or Text Summarizer for a fuller picture of a document.
- Remember bigrams catch context that single words miss, so scan both lists.
The Keyword Extractor is a fast, transparent way to see what a text is really about. It will not guess at search intent, but for surfacing the terms that carry your message, frequency is a surprisingly powerful and entirely private signal.
Mining text for the words in it
Extraction counts what is actually in your text: single words, and (where enabled) the repeated two- and three-word phrases that carry meaning. The output is a frequency list, not a recommendation — but a high-frequency phrase in a competitor page, or a recurring term across your own docs, is groundwork for topics and tags. Strip stop words first (“the”, “and”, “for”) or the list drowns in filler and nothing of value surfaces.
- Job ads: extract skills, then map them to your CV phrasing.
- Articles: extract the top phrases, and you have a candidate title and tags.
- Support tickets: cluster by the extracted product terms before routing.
Frequently asked questions
Paste the text into the Keyword Extractor. It strips stopwords, counts the remaining single words and two-word phrases, and lists the top ones by frequency so you can copy them.
Commonly for SEO audits, generating tags and metadata for blog posts, spotting the dominant topics of a long document, and comparing which terms a piece of text really emphasizes.
