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Anchor Text Relevance Checker

Use cases

Auditing internal link quality Finding irrelevant anchor text to fix Training content teams on anchor best practices Improving topical relevance signals

GPT-4 semantic evaluation of anchor text against target page.

Three-dimension analysis: relevance accuracy, language naturalness, citation quality.

Four ratings: High, Medium, Fail, Typo.

Auto-fail criteria: questions, sentence fragments.

Batch processing with column mapping.

Streamlit App Requires API Key

Platform

Browser-based (no installation required)

Input

OpenAI API key

Anchor text and target URL pairs

Optional: H1 and title for context

Output

Relevance rating per pair with JSON schema output. Bulk export: anchor, URL, metadata, rating category.

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Features

  • Three-dimension analysis (relevance/naturalness/citation)
  • Four-level ratings: High, Medium, Fail, Typo
  • Auto-fail for questions and fragments
  • Batch processing with column mapping
  • Optional H1 and title context

How to use

  1. 1 Enter your OpenAI API key
  2. 2 Upload CSV/Excel with anchor-URL pairs
  3. 3 Map columns (anchor, URL, optional H1/title)
  4. 4 Run semantic relevance check
  5. 5 Export with ratings: High/Medium/Fail/Typo

Want me to run this for you?

I offer this as a managed service. You get the insights without touching the tool.

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