Density Engine.
Dual-document keyword frequency matrix. Analyze term density across competing content clusters instantly.
| Target Keyword | Doc A Analysis | Doc B Analysis |
|---|
Density Engine: Keyword Matrix Analysis
An advanced analytical deep dive on keyword density distributions, competitive TF-IDF mappings, natural entity variance models, and algorithmic search penalties.
How to Use the Density Engine
Set Target Keywords
Input your target search phrases (separated by commas or newlines) inside the Target Variables configuration panel.
Paste Documents
Paste the text of Document A (e.g., your draft copy) and Document B (e.g., a competitor's page) inside their respective panels.
Execute Analysis
Click Execute Matrix Scan. The matrix table immediately lists keyword counts and density classifications for both text files.
Keyword Density Optimization & Spam Penalties
Search engine ranking models evaluate term frequencies to categorize page content. Over-using target terms (known as Keyword Stuffing) triggers search filters that drop your visibility.
Keep your primary term density within the 1.0% to 3.0% sweet spot. Ratios higher than 3.0% look unnatural, while values below 1.0% can make it hard for search engines to determine relevance.
- Optimal (1.0% - 3.0%): Highest Rank Potential
- Stuffed (> 3.0%): Spam Filter Risk
- Low (< 1.0%): Increase Term Variance
TF-IDF Vector Mappings
Measures how often a target keyword appears inside a single document relative to the overall length.
Evaluates how unique or common a term is across all competitor pages in the same search vertical.
Competitive Term Balance (TF-IDF)
Search engines use TF-IDF equations to evaluate the authority of matching pages. By comparing your copy against top ranking competitor pages, you identify optimization gaps.
Rather than copying competitor structures blindly, balance your term distribution naturally. This matches the keyword density expected by search engines while keeping your text unique.