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Keyword Density Checker & N-Gram Analyzer
The Keyword Density Checker analyzes text to extract single words, 2-word phrases (bigrams), and 3-word phrases (trigrams) alongside exact frequency counts and percentage density metrics. It features built-in stop-word removal, lexical diversity scoring, sortable columns, and instant CSV export to prevent keyword stuffing.
Text Content Analyzer
Min Word Length:
0
Total Words
0
Unique Words
0%
Lexical Diversity
| # | Keyword / Phrase ↕ | Repeats ↕ | Density % ↕ |
|---|
Paste content into the box above and click "Analyze Text" (or "Load Sample Text") to see keyword density statistics.
How to Use the Keyword Density Checker
Follow this step-by-step workflow to audit and balance the keyword distribution in your content:
- Paste Your Content: Copy your written draft or the published body text of a competitor's page and paste it into the primary input area.
- Configure Stop Words & Length: Leave the Filter Common Stop Words toggle checked to ignore grammatical connector words like "the", "and", and "in". Adjust the minimum character length to filter out 1- or 2-letter fragments.
- Examine High-Level Metrics: Check the summary dashboard cards to observe Total Words, Unique Words, and your Lexical Diversity score. Aim for a lexical diversity above 35% on standard informational articles.
- Inspect N-Gram Tabs: Switch between the 1-Word, 2-Word, and 3-Word tabs to identify how often key industry phrases and topic clusters recur throughout the copy.
- Sort Columns to Detect Overuse: Click on the Density % header to sort the table descending. Any term exceeding 3% density should be evaluated for potential keyword stuffing.
- Export CSV for Editorial Teams: Click Export CSV to generate an exportable spreadsheet report detailing all phrase frequencies for your content writers.
What is Keyword Density in SEO?
Keyword density represents the percentage of times a specific keyword or phrase appears within a body of text relative to the total number of words. The standard mathematical formula is expressed as:
Keyword Density (%) = (Keyword Frequency × Number of Words in Phrase / Total Words in Text) × 100
In early search engine algorithms, keyword density was a primary ranking signal. Webmasters routinely inflated keyword counts (a practice known as keyword stuffing) to manipulate search rankings. However, modern search engines—powered by natural language processing (NLP) models like BERT, RankBrain, and transformer-based neural architectures—understand semantic context, co-occurring entities, and topical coverage. While strict mathematical density is no longer a magic ranking factor, monitoring density remains essential to prevent over-optimization penalties and verify natural readability.
Key Features of This Analyzer
- Multi-Word N-Gram Detection: Automatically extracts bi-grams (2 words) and tri-grams (3 words) to surface long-tail keyword combinations and brand phrases.
- Comprehensive Stopword Filtering: Strips over 120 common English function words, leaving only substantive nouns, verbs, and subject terms for evaluation.
- Interactive Sortable Table: Instantly sort any column in ascending or descending order with visual percentage bar representations.
- Lexical Diversity Metric: Calculates the type-token ratio (TTR) to provide objective insight into the linguistic richness and vocabulary variety of your writing.
- Client-Side CSV Generator: Exports report data directly using browser-generated Data Blobs without requiring server-side export endpoints.
Who Needs a Keyword Density Checker?
- Content Editors & Proofreaders: Spot accidental phrase repetition and ensure that writer submissions maintain an engaging, natural flow.
- SEO Copywriters: Confirm that target focus terms appear naturally throughout the document without crossing into spam territory.
- Competitive Intelligence Analysts: Paste competitor ranking pages into the tool to identify their core topical n-grams and vocabulary choices.
- Academic & Essay Writers: Evaluate lexical diversity and eliminate repetitive transitional phrases before submitting manuscripts.
Best Practices vs. Common Mistakes in Keyword Density
| Optimization Factor | Recommended Best Practice | Common Mistake to Avoid |
|---|---|---|
| Primary Term Density | Maintain between 1.0% and 2.5% for the primary target keyword. | Exceeding 4%, which makes text unreadable and invites algorithmic penalties. |
| Synonyms & LSI Terms | Incorporate natural variations, related concepts, and antonyms. | Repeating the exact same match phrase in every single paragraph. |
| Structural Placement | Include target phrases naturally in H1, early body text, and one subheader. | Stuffing keywords into every sub-heading (H2/H3) and image alt attribute. |
| Audience Alignment | Write primarily for human clarity, comprehension, and actionable value. | Sacrificing sentence flow and grammar to hit an arbitrary mathematical ratio. |
Comparing Keyword Density, TF-IDF, and Semantic Salience
| Methodology | Definition & Calculation | Primary Use in Modern Search |
|---|---|---|
| Keyword Density | Term count divided by total document words. | Internal editorial sanity check to avoid over-optimization and keyword stuffing. |
| TF-IDF | Term Frequency multiplied by Inverse Document Frequency across a corpus. | Identifies words that uniquely characterize an article compared to all documents. |
| Semantic Salience | Neural network entity scoring based on vector embeddings and contextual graphs. | Used by Google and AI engines to determine how deeply a page answers user intent. |
Privacy: Everything Runs in Your Browser
Your text never touches an external server. Many online content auditing tools upload your drafts to cloud databases or utilize them to train generative models. BizBop processes 100% of your copy locally via JavaScript in your device's memory. When you refresh or close this tab, your text is completely erased.
Frequently Asked Questions
Most SEO specialists recommend an unigram keyword density between 1% and 2.5% for your primary search phrase. Exceeding 3% risks triggering Google's helpful content and keyword stuffing spam filters, while maintaining under 1% may fail to establish topical focus.
An n-gram is a contiguous sequence of n words from a given text. Analyzing 2-word bigrams and 3-word trigrams reveals long-tail keyword combinations and semantic entities that single-word tokenizers overlook.
Stop words (e.g., 'the', 'is', 'at', 'which', 'and') naturally account for 30% to 50% of any English text. Filtering them removes grammatical noise, enabling you to inspect the real topical substance of your article.
Lexical diversity calculates the ratio of unique words to total words in your document. A higher percentage indicates richer vocabulary and diverse phrasing, which signals high editorial quality to both human readers and search algorithms.
No. The entire tokenization and frequency calculation process runs in client-side JavaScript within your browser tab. Your drafts, client articles, and proprietary content remain strictly private.
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