Keyword Extractor
Extract the top keywords and phrases from any text or web page URL.
How to use Keyword Extractor
- 1Paste your article draft, blog post, or competitor webpage URL into the input field.
- 2Click 'Extract Keywords' to initiate natural language tokenization and frequency analysis.
- 3Inspect the breakdown of single keywords (unigrams), two-word phrases (bigrams), and three-word phrases (trigrams).
- 4Review keyword density percentages to ensure your primary search terms are naturally distributed.
- 5Identify over-optimized terms to eliminate keyword stuffing before publishing.
Key Features & Highlights
- •Multi-Input Processing: Analyze raw pasted text or fetch live web pages directly via URL.
- •N-Gram Phrase Extraction: Discovers 1-word, 2-word, and 3-word phrase frequency patterns for long-tail keyword targeting.
- •Intelligent Stop-Word Filter: Automatically strips out common grammatical fillers (e.g., 'the', 'and', 'is', 'at') to isolate meaningful semantic terms.
- •Real-Time Keyword Density Telemetry: Calculates exact occurrence counts and density percentages relative to total word count.
- •100% Free & Unlimited: Audit unlimited blog posts, landing pages, and competitor articles.
Understanding Keyword Extractor
Frequently Asked Questions
What is an ideal keyword density for SEO content?
Most search engine optimization experts recommend maintaining a primary keyword density between 1% and 2% (approximately 1 to 2 mentions per 100 words). Exceeding 3% can trigger algorithmic over-optimization or keyword stuffing penalties, while maintaining natural language flow and topical coverage is what Google's modern helpful content system rewards.
What are N-grams and why do 2-word and 3-word phrases matter for SEO?
An N-gram is a contiguous sequence of n items from a given sample of text. Unigrams are single words, Bigrams are 2-word phrases (e.g. 'video converter'), and Trigrams are 3-word phrases (e.g. 'best video converter'). Extracting 2-word and 3-word phrases reveals high-intent long-tail keywords that users frequently type into search engines.
How does Google evaluate keywords with semantic search and NLP?
Modern search algorithms (powered by BERT, MUM, and Gemini) rely on Natural Language Processing (NLP) to understand entities, concepts, and context rather than counting exact keyword occurrences. Including related terms, synonyms, and sub-topics (semantic keywords) signals topical depth and authority to search engines far better than repeating a single keyword.
What is keyword stuffing and how does this tool prevent it?
Keyword stuffing is the practice of unnaturally cramming search keywords into web content to manipulate rankings, which violates Google Search Essentials and can result in rank demotion. This tool calculates exact density percentages, allowing you to quickly spot terms appearing with disproportionately high frequency and adjust them to natural variations.
What are stop words and why are they removed during extraction?
Stop words are common grammatical function words such as 'the', 'is', 'in', 'at', 'and', or 'which' that occur frequently in speech but carry minimal distinctive topical meaning. Filtering out stop words allows the extractor to surface the true thematic nouns, verbs, and subject phrases that define what your content is about.
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