Semantic Drift Analyzer

Semantic Drift Analyzer

Visualize and measure how far your query fan-outs drift from the original search intent

? How to Use

1

Enter Target Query

Paste your primary search query in the first field

2

Add Fan-Outs

List query fan-outs (one per line) that you want to analyze

3

Analyze Drift

Click the analyze button to calculate semantic distances

4

Review Results

Examine the 3D visualization and drift scores, then export if needed

Bulk Processing via CSV Upload

Process multiple query pairs at once by uploading a CSV file. Your CSV must contain exactly two columns with headers: Target Query and Query Fan-Outs. Each row represents a target query and its corresponding fan-out variation.

CSV Format Requirements:

Target Query,Query Fan-Outs
“best running shoes”,”running shoes for beginners”
“best running shoes”,”marathon training shoes”
“best running shoes”,”athletic footwear reviews”
  • First row must be headers: Target Query,Query Fan-Outs
  • Each subsequent row contains one target query and one fan-out query
  • Wrap queries in quotes if they contain commas
  • Results will automatically download as a CSV file

Semantic Boost (USE)

Runs locally with Universal Sentence Encoder to capture synonyms & context. No API keys.

3D Vector Space Visualization

The central white node represents your target query. Colored nodes show query fan-outs, positioned by semantic distance. Hover over nodes to see details.

Controls: Left-click + drag to rotate • Right-click + drag to pan • Scroll to zoom
Drift Scale
Target Query Center
Minimal Drift 0-30%
Low Drift 30-50%
Medium Drift 50-70%
High Drift 70-85%
Extreme Drift 85-100%

How Scores are Calculated

Key Benefits for SEO & Content Strategy

Maintain Search Intent

Ensure your query expansions stay aligned with the original user intent, preventing keyword cannibalization and irrelevant traffic.

Quantify Semantic Distance

Get objective metrics on how far your query fan-outs drift from the target, enabling data-driven content decisions.

Optimize Query Clustering

Identify which queries should be grouped together vs. targeted separately based on semantic similarity scores.

Improve Content Relevance

Create content that accurately matches user expectations by understanding the semantic relationships between queries.

Better Keyword Strategy

Build more cohesive keyword clusters and topic hierarchies by visualizing semantic relationships in 3D space.

Discover Opportunities

Find new angles and content opportunities by exploring queries with moderate drift that still maintain core intent.