Sentiment Visualization

When you look at a traffic light, you don't read the word 'stop.' You see red. Color communicates faster than language. Persephonie uses this principle to encode the sentiment of every outcome: emerald for positive, rose for negative, gray for neutral.

Instant Evaluation

In a text-based AI response, you have to read every outcome to evaluate it. 'This option has moderate risk but high reward, while this option is safer but limits growth...' By the time you finish parsing the language, you've forgotten the first option.

In a tree, outcomes are color-coded. Glance at a branch and you see: two green outcomes, one red risk. Evaluation is instant. You haven't read a word and you already know the general shape of each path.

If you have to read the outcome to know if it's good or bad, the interface has failed.

The Sentiment Palette

• Emerald: positive outcomes, opportunities, growth
• Rose: risks, downsides, potential problems
• Gray: neutral outcomes, trade-offs, unknowns
• Teal: active exploration, the path you're currently on

Color and Confidence

Sentiment visualization does something subtle but powerful: it gives users confidence in their evaluation. When you can see the balance of green and red across two options, you feel informed. You feel like you understand the trade-offs without having to hold paragraphs of text in your head. That confidence leads to better, faster decisions.