Keyword Extractor & N-Gram Density Analyzer
Extract single terms, bigrams, and long-tail trigrams with sentence-boundary awareness and verified frequency density ratios.
Input Content Draft or Article Body
Paste text above and click "Extract Keywords" to generate N-gram frequency tables.
1. The Role of Lexical N-Grams in Search Content Auditing
Search engines index documents by evaluating topic relevance and lexical distributions. Rather than analyzing single words in isolation, modern search algorithms examine contiguous sequences of tokens—known as N-grams—to understand compound concepts and multi-word entities.
For example, the individual words "relational" and "database" carry different semantic implications than the unified bigram "relational database". Identifying multi-word keyword density ensures your article thoroughly addresses high-intent search queries without relying on mechanical keyword stuffing.
2. Sentence Boundaries & N-Gram Structural Rules
High-accuracy text analysis requires that phrase windows respect punctuation and structural boundaries:
| N-Gram Tier | Token Window ($N$) | Boundary Constraint | Linguistic Role |
|---|---|---|---|
| Unigrams (1-Word) | 1 word | Isolated content words | Identifies primary subject nouns, verbs, and core terminology. |
| Bigrams (2-Word) | 2 contiguous words | Must belong to the same sentence | Captures compound commercial search terms (e.g., "cloud database"). |
| Trigrams (3-Word) | 3 contiguous words | Must belong to the same sentence | Identifies conversational and transactional intent (e.g., "how to optimize"). |
3. Understanding Keyword Density Calculation
In search engine optimization, keyword density represents the frequency of a term relative to the total volume of text:
Density (%) = (Phrase Occurrences / Total Words in Document) × 100
For primary focus terms, maintaining a density between 1.0% and 2.5% is generally recommended. Density exceeding 3.5% risks appearing unnatural to readers and triggering over-optimization filters. For 2-word and 3-word phrases, natural densities are typically lower (often between 0.4% and 1.5%).
4. 4 Content Optimization Best Practices
- Prioritize Context Over Fixed Ratios: Modern search engines prioritize comprehensive topic coverage and entity relationships over rigid density percentages.
- Audit Trigrams for Natural Flow: If a 3-word phrase appears with unusually high density, rephrase secondary occurrences with conversational synonyms.
- Filter Common Stop Words: Discarding high-frequency structural words (such as the, and, with) ensures your keyword table highlights genuine topical terminology.
- Respect Natural Sentence Flow: Never insert keywords where they disrupt grammatical clarity. User experience and readability remain the primary drivers of engagement.
5. Explore Related SEO & Content Utilities
Streamline your content development, search optimization, and link hygiene workflows with these free utilities:
6. Frequently Asked Questions (FAQs)
How does this keyword extractor parse text?
The tool parses text into sentence segments to prevent N-grams from spanning across periods or line breaks. It normalizes case, removes punctuation, filters common English stop words, and groups contiguous tokens into unigrams (1-word), bigrams (2-word), and trigrams (3-word).
How is keyword density calculated?
Keyword density is calculated as the total number of times a specific term or phrase appears divided by the total word count of the analyzed text, expressed as a percentage.
Why do 2-word and 3-word N-grams not cross sentence boundaries?
In natural language processing, words on opposite sides of a terminal period or paragraph break do not form a semantic phrase. Restricting N-gram windows within sentence boundaries ensures high data accuracy.
Is my text content uploaded or stored on your servers?
No. All tokenization, filtering, and frequency calculations execute entirely inside your local browser sandbox using client-side JavaScript. No document content is transmitted over external networks.