IntentEmoji Documentation

IntentEmoji is the first open specification for intentional emoji placement. It treats emoji as cognitive architecture, not decoration. Every emoji in your text either primes, punctuates, or annotates. IntentEmoji gives you a system for knowing which one you are doing, and doing it on purpose.

The Core Thesis

Most emoji today is placed after the thought it relates to. The sentence makes its point, and then the emoji arrives to react. This is called postloading, and it is the default behavior for nearly everyone who uses emoji in text.

IntentEmoji is built on a different approach. When an emoji appears before the text it relates to, it primes the reader's cognition. The brain receives an emotional or semantic signal first, and then the words land inside that frame. This is called preloading, and it is measurably more effective at capturing attention, increasing comprehension, and driving engagement.

The distinction is simple. The impact is significant. The research supports it. And until now, nobody had turned it into a usable system.

Quick Start

Option 1: API

Send plain text to the API and get back enhanced text with intentional emoji placement.

curl -X POST https://api.intentemoji.com/v1/enhance \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "We just shipped the new dashboard. Users can now track metrics in real time.",
    "profile": "professional",
    "density": "moderate"
  }'

Option 2: SDK

Install the JavaScript or Python package and call the enhance method directly.

npm install intentemoji

import { IntentEmoji } from 'intentemoji';

const ie = new IntentEmoji({ apiKey: 'YOUR_API_KEY' });
const result = await ie.enhance(
  'We just shipped the new dashboard.',
  { profile: 'professional' }
);
console.log(result.text);

Option 3: Browser Extension

Install the extension and use Read Mode to see what any page is missing, or Compose Mode to enhance your writing in real time as you type.

Option 4: AI Prompt Module

Copy the IntentEmoji system prompt block into any LLM's system instructions. The model will produce IntentEmoji-compliant output without any API calls.

Explore the Docs