🧠 The Science Behind It
This tool uses text embeddings to understand the semantic meaning of text and position it on custom spectrums you define. It's powered by Google's Universal Sentence Encoder, a neural network.
🔧 How It Works
1. Spectrum Definition: You define two opposing concepts (like "formal" vs "casual" or "negative" vs "positive")
2. AI Embeddings: The system converts your spectrum concepts and each sentence into high-dimensional vectors (512 dimensions) that capture semantic meaning
3. Similarity Calculation: Using cosine similarity, it measures how close each sentence is to either end of your spectrum
4. Visual Mapping: Sentences are colored based on their position on the spectrum
🎨 Color Coding
Left spectrum (your first concept)
Right spectrum (your second concept)
💡 Example Use Cases
Sentiment Analysis: "pessimistic" → "optimistic"
Tone Analysis: "formal" → "casual"
Certainty Analysis: "uncertain" → "confident"
Complexity Analysis: "simple" → "technical"
nbYou can use phrases too to signal the ends of the spectrum you're after!
🎯 Tips for Best Results
- Use clear opposites: Choose concepts that are genuinely opposite to each other
- Be specific: Instead of "good" vs "bad", try "disappointing" vs "excellent"
- Single concepts: Use single words or short phrases rather than sentences
- Consistent language: Keep the same language/style for both spectrum ends
⚙️ Technical Details
Model: Universal Sentence Encoder (USE) by Google Research
Embedding Dimensions: 512-dimensional vectors
Similarity Metric: Cosine similarity between sentence and spectrum embeddings
Processing: Client-side AI using TensorFlow.js (your data never leaves your browser)
🔒 Privacy
All analysis happens locally in your browser. Your text and spectrum definitions are never sent to any server - everything runs using AI models downloaded to your device.