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Keywords will appear here
Paste text and click Extract Keywords// extract keywords and phrases from any text instantly
Extract and analyze frequent keywords and phrases from any article, note, or draft. Free browser-based keyword frequency analyzer — no sign-up required.
Keywords will appear here
Paste text and click Extract KeywordsDrop in any article, blog post, notes, product description, or draft — up to 200,000 characters.
Set max phrase length (1–4 words), minimum frequency threshold, and whether to filter common stop words.
Click Extract Keywords to see a ranked frequency table plus a visual word cloud. Copy results as CSV for further use.
The Keyword Extractor scans any text and identifies the most frequently occurring words and multi-word phrases. It uses n-gram analysis to find not just individual words but meaningful compound expressions like "machine learning" or "open source." Stop word filtering removes noise like "the," "and," and "is" so you see only the semantically meaningful content.
Keyword extraction is the process of automatically identifying the most significant and frequently used words or phrases in a body of text. It helps you quickly understand the main topics and themes of any document without reading it in full.
Stop words are extremely common words like "the," "and," "is," "a," and "of" that carry little semantic meaning on their own. Filtering them removes noise from results so you see only the meaningful content words that reveal the actual topics of your text.
An N-gram is a contiguous sequence of N words. By extracting 2-word or 3-word phrases (bigrams, trigrams), you capture compound concepts like "artificial intelligence," "user interface," or "open source project" that single-word analysis would miss entirely.
Keyword density is the percentage of times a keyword appears relative to the total word count in the text. For example, if "JavaScript" appears 10 times in a 1,000-word article, its density is 1.00%. SEO best practice typically suggests keeping main keyword density between 0.5% and 2%.
The tool supports up to 200,000 characters — roughly the equivalent of a full novel chapter or a very long research report. All processing is done server-side with PHP for accuracy, while results are returned instantly via API call.
No. Your text is processed on the server only to compute keyword frequencies and is never stored, logged, or shared. The tool does not retain any input between requests.
Yes. Click the "Copy CSV" button to copy all keyword results to your clipboard in comma-separated format, ready to paste into Excel, Google Sheets, or any data tool for further analysis.
When filtering stop words, single tokens that appear at the start or end of multi-word phrases are also filtered individually if they're in the stop word list. You can uncheck "Filter stop words" to see all tokens including common function words.
A keyword extractor is a text analysis tool that automatically scans a body of text and identifies the most frequently occurring words and phrases. Instead of reading through an entire document manually to understand its core topics, a keyword extractor gives you an instant, ranked view of what a piece of writing is really about — measured by raw frequency and statistical density.
Whether you're an SEO specialist analyzing existing content, a writer reviewing a draft for repetitive language, a researcher summarizing a report, or a content strategist mapping themes across multiple articles, keyword extraction transforms raw text into actionable intelligence in seconds.
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The extraction process involves several steps working together. First, the raw text is normalized — converted to lowercase, stripped of punctuation, and split into individual tokens (words). Then, for each desired phrase length (called an N-gram), the tool counts how many times each unique sequence of words appears in the text.
Stop word filtering is applied to remove semantically empty words — articles, prepositions, conjunctions, auxiliary verbs — that would otherwise dominate the results without providing useful information. What remains is a clean frequency table of the words and phrases that actually define the content's meaning.
Finally, keyword density is calculated: the percentage of times a term appears relative to the total word count. This metric is especially useful for SEO analysis, where maintaining natural keyword density is essential for search ranking without triggering over-optimization penalties.
One of the most valuable features of a modern keyword extractor is N-gram phrase detection. Analyzing only single words misses the most informative patterns in language. Consider these examples:
By setting the max phrase length to 2, 3, or 4 words, you uncover compound concepts and recurring expressions that reveal how topics are actually discussed in your text, not just what individual vocabulary is used.
SEO Content Optimization: Before publishing, paste your article into the extractor and verify that your target keywords appear at the appropriate density. Identify unintentional keyword stuffing or discover secondary keywords you've organically used that could be targeted more deliberately in meta tags and headings.
Content Auditing: When reviewing a large collection of blog posts or web pages, keyword extraction helps identify whether each piece has a clear topical focus or whether the content drifts across too many unrelated subjects — a common cause of poor search performance.
Academic and Research Writing: Writers working on research papers can use keyword extraction to ensure consistent use of technical terminology, check that key concepts are introduced and repeated at appropriate intervals, and verify that their abstract accurately reflects the paper's dominant themes.
Competitive Analysis: Copy the text from a competitor's webpage or blog post and run keyword extraction to understand what topics they're emphasizing, which phrases they repeat for SEO effect, and where there might be gaps you can fill with your own content strategy.
Meeting Notes and Transcripts: Extract keywords from meeting transcripts, interview recordings, or customer feedback surveys to quickly identify the most commonly raised topics, concerns, or requests without reading every word.
Keyword density measures how often a specific keyword appears as a percentage of total word count. For decades, SEO practitioners have debated the "ideal" keyword density, but modern search engine algorithms — especially Google's — have moved far beyond simple frequency counting toward semantic understanding of content.
That said, density analysis remains a useful signal. A density below 0.5% may indicate underuse of your target keyword, making it harder for search engines to understand the page's topic. A density above 3–4% starts to look unnatural and may trigger algorithmic quality filters. Most well-optimized content sits in the 0.5%–2% range for primary keywords.
More importantly, paying attention to phrase density helps you ensure that related concepts and synonyms appear naturally throughout your content — a pattern that contributes to topical authority in modern semantic search systems.
For the most useful extraction results, consider these practices:
It's worth understanding the limits of pure frequency analysis. A keyword extractor tells you how often terms appear, but not necessarily their semantic importance or contextual meaning. A word might appear frequently because it's a topic itself, or simply because it's used as a transitional device in a particular author's writing style.
For deeper analysis, frequency data is best combined with other signals: TF-IDF scoring (which weighs term frequency against how common the term is across many documents), semantic clustering, and manual review of the surrounding context for the highest-frequency terms. Think of keyword extraction as the first step in a content intelligence workflow, not the complete picture.