Semantic Text Analysis Tool

Text analysis bridges the gap between creative writing and algorithmic search engine optimization. Search engines rely on semantic density and structural patterns to determine relevance. Writers often struggle to balance natural flow with the mechanical requirements of SEO. Overusing specific terms triggers spam filters, while underusing them results in poor ranking. This tool parses raw text to extract quantitative metrics. It identifies the most frequent semantic units by filtering out grammatical noise (stop words) and consolidating variations through lemmatization. This ensures that terms like "run", "running", and "ran" are treated as a single conceptual entity. Accuracy in these metrics prevents over-optimization penalties and ensures content meets readability standards for the target audience.

The core metric for SEO safety is Keyword Density (ρ), which represents the ratio of a specific keyword's frequency to the total word count. However, modern analysis also considers the "Classic Nausea" index, often calculated as the square root of the absolute frequency of the most common word.

Where f_k is the frequency of the keyword and N_total is the total word count excluding stop words (in some models) or including them (in strict density models).

This formula estimates the grade level required to understand the text. High values indicate complex sentence structures.