Profanity Detector API for Node.js

Detect profanity and inappropriate language in text -- powered by SharpAPI AI.

SharpAPI Profanity Detector analyzes text content to identify profane, offensive, or inappropriate language. Returns a sanitized version of the text along with a list of detected profanities. Capable of detecting obfuscation techniques including character substitutions (e.g., @ for a), divided words, and letter repetitions.

Table of Contents

1. Requirements
2. Installation
3. Usage
4. Use Cases
5. License

Requirements

• Node.js >= 16.x
• npm or yarn

Installation

Step 1. Install the package via npm:

npm install @sharpapi/sharpapi-node-detect-profanities

Step 2. Get your API key

Visit SharpAPI.com to get your API key.

Usage

const { SharpApiDetectProfanitiesService } = require('@sharpapi/sharpapi-node-detect-profanities');

const apiKey = process.env.SHARP_API_KEY; // Store your API key in environment variables
const service = new SharpApiDetectProfanitiesService(apiKey);

const text = 'Some user-generated content that needs to be checked for inappropriate language.';

async function checkContent() {
  try {
    // Submit processing job
    const statusUrl = await service.detectProfanities(text);
    console.log('Job submitted. Status URL:', statusUrl);

    // Fetch results (polls automatically until complete)
    const result = await service.fetchResults(statusUrl);
    console.log('Result:', result.getResultJson());
  } catch (error) {
    console.error('Error:', error.message);
  }
}

checkContent();

API Documentation

Methods

detectProfanities(text)

Analyzes the provided text for profanity, offensive language, and obfuscated inappropriate words.

Parameters:

• text (string, required): The text content to analyze

Returns: Promise - Status URL for polling results

Detection Capabilities

The AI-powered detector goes beyond simple word matching. It can identify:

• Direct profanity: Standard offensive words and phrases
• Character substitutions: @ss, sh!t, f*ck and similar obfuscations
• Divided words: Words split with spaces or punctuation to evade filters
• Letter repetitions
• Mixed techniques: Combinations of the above obfuscation methods

Use Cases

• Content Moderation: Filter inappropriate language from comments, reviews, and posts
• Community Management: Maintain safe environments in forums, chats, and social platforms
• User Protection: Prevent cyberbullying and harassment in real-time
• Brand Safety: Ensure user-generated content aligns with brand values
• Compliance: Meet content policies and regulatory requirements (COPPA, platform TOS)
• Parental Controls: Filter inappropriate content for younger audiences
• Anti-Evasion: Catch obfuscated profanity that simple word filters miss

License

This project is licensed under the MIT License. See the LICENSE.md file for details.

Support

• Documentation: SharpAPI.com Documentation
• Issues: GitHub Issues
• Email: [email protected]