What types of text patterns can the API detect?

The API detects over 15 distinct pattern types including email addresses, phone numbers (domestic and international formats), URLs, credit card numbers with Luhn validation, social security numbers, dates in multiple formats, IPv4/IPv6 addresses, and postal codes. Beyond regex-based pattern matching, the API uses transformer-based NLP models for named entity recognition (NER), identifying people, organizations, locations, monetary values, and temporal expressions. Each detected pattern includes character offsets, confidence scores, and contextual metadata, enabling precise extraction from unstructured text for compliance scanning, data loss prevention, and content enrichment workflows.

What file formats are supported for analysis?

The API supports direct text input via JSON payloads as well as file uploads in PDF, DOCX, TXT, HTML, and RTF formats. You can also analyze content directly from URLs, where the API fetches the page, strips boilerplate navigation and ads using readability algorithms, and processes the main content body. For PDF documents, the API handles both digitally-created PDFs and scanned documents through integrated OCR. DOCX processing preserves structural elements like headings, lists, and tables. The maximum file size is 10MB, and the API automatically detects character encoding for TXT files, supporting UTF-8, Latin-1, and other common encodings.

How does the sentiment analysis work?

The sentiment analysis engine uses a fine-tuned transformer model trained on millions of labeled text samples across multiple domains including product reviews, social media posts, news articles, and customer support transcripts. It returns a normalized sentiment score between -1.0 (strongly negative) and 1.0 (strongly positive), along with a categorical label (positive, negative, neutral, or mixed). The API also provides aspect-based sentiment analysis, which identifies specific topics or entities within the text and assigns individual sentiment scores to each. For example, a restaurant review mentioning both food and service would receive separate sentiment scores for each aspect, enabling granular opinion mining.

What languages does the API support?

The Text Analysis API supports over 50 languages for language detection with confidence scoring, and provides full NLP analysis (sentiment, entities, topics, keywords) for 12 primary languages: English, Spanish, French, German, Portuguese, Italian, Dutch, Russian, Chinese (Simplified and Traditional), Japanese, and Korean. Language detection works on text as short as 20 characters and can identify mixed-language documents, reporting the percentage breakdown of each detected language. For non-primary languages, the API still provides pattern detection, language identification, and basic entity extraction. The sentiment model supports cross-lingual transfer learning, delivering reliable results even for languages outside the primary set.

Can I run multiple analysis types in a single request?

Yes, the API is designed for efficient batch analysis within a single request. You pass an array of analysis types in the request body, such as ["patterns", "entities", "sentiment", "language", "topics", "keywords", "summary", "moderation"]. Each analysis module runs in parallel on our infrastructure, so requesting multiple types adds minimal latency compared to a single analysis. The response includes a dedicated section for each requested type. You can also define custom analysis rules using the "custom" type, which accepts regex patterns, dictionaries, or classification labels. This approach eliminates the need for multiple API calls and reduces overall processing time by up to 80% compared to sequential single-type requests.

How does the content moderation feature work?

The content moderation module evaluates text against multiple safety categories including hate speech, harassment, violence, sexual content, self-harm, and spam. Each category receives an individual risk score from 0.0 to 1.0, along with an overall safety rating and a recommended action (allow, flag, or block). The model is trained on diverse datasets and regularly updated to address emerging patterns of harmful content. It handles obfuscated text, intentional misspellings, and coded language that simple keyword filters would miss. The moderation engine also provides explanation snippets that highlight the specific passages triggering each flag, making it suitable for human-in-the-loop review workflows. Configurable threshold settings allow you to adjust sensitivity levels for different deployment contexts.

AI-powered text analysis for modern applications

Text Analysis API

Comprehensive text analysis API that combines advanced pattern detection with cutting-edge NLP analysis. Extract insights from any text content with support for multiple input formats and real-time processing.

Powered by state-of-the-art AI technology, our API delivers accurate results for pattern recognition, sentiment analysis, and entity extraction across multiple languages and content types.

Get a free API key Read the docs

Free tier included, no credit card required. One key works across every API.

Free calls / month: 40
Accuracy Rate: 98.5%
Languages Supported: 50+
Pattern Types: 15+

Get your free API key

40 free calls on the free plan

Key features

Multi-pattern detection in single request
Real-time sentiment & emotion analysis
Named entity recognition (NER)
Topic modeling & keyword extraction
Content moderation & safety scoring
Multi-format input support

Built for

Content management systems
Social media monitoring
Customer feedback analysis
Document processing pipelines
Compliance & risk assessment
Research & data mining

MCP · for your agent

Hand the Text Analysis API to your agent

Connect Claude, Cursor or any MCP client once, then ask in plain words. Your agent finds this API, reads its docs and makes the call: 25 credits per call, the same as REST, from your team's balance.

Connect your agent

Find themes in customer feedback

“Here are 200 NPS comments from last quarter. What are the main topics, and how does sentiment differ between them?”

list_apis · get_api_docsfree

api_request POST /text-analysis/v1/analyze/text 25 cr

Sentiment, topics and keywords per comment, rolled up into themes with their overall mood.

Summarize a competitor's page

“Summarize Fabrikam's new pricing page for our battlecard: key points, keywords and any figures they mention.”

list_apis · get_api_docsfree

api_request POST /text-analysis/v1/analyze/url 25 cr

A summary, keywords, entities and figures from the page, formatted for the battlecard.

Moderate community posts

“Screen the latest 50 posts in our r/acmehq subreddit for toxic content and any leaked emails or phone numbers.”

list_apis · get_api_docsfree

api_request POST /text-analysis/v1/analyze/text 25 cr

+ Advanced Reddit API25 cr

Moderation flags plus detected emails and phone numbers per post, queued for the mods.

$ claude mcp add --transport http apicodex https://mcp.apicodex.io

Illustrative asks; the calls and prices are real. Claude, Cursor, VS Code and Windsurf setup: mcp.apicodex.io

Try it

Build the request for your data

Edit the parameters below and see the exact request and curl command you'd send. No key required to preview.

POST /text-analysis/v1/analyze/text

Open in Playground

Request · HTTP

POST /text-analysis/v1/analyze/text HTTP/1.1
Host: api.apicodex.io
Content-Type: application/json
X-Api-Key: YOUR_API_KEY

{
  "text": "Apple Inc. announced new products in California yesterday.",
  "analysis": ["patterns", "entities", "sentiment", "language"]
}

Response · JSON 200 OK

{
  "success": true,
  "language": { "detected": "en", "confidence": 0.99 },
  "sentiment": { "score": 0.65, "label": "positive" },
  "entities": [
    { "type": "ORG", "value": "Apple Inc.", "start": 0, "end": 10 },
    { "type": "LOCATION", "value": "California", "start": 39, "end": 49 },
    { "type": "DATE", "value": "yesterday", "start": 50, "end": 59 }
  ],
  "patterns": { "emails": [], "urls": [], "dates": ["yesterday"] }
}

Run it with your key

Documentation

Text Analysis API Documentation

Learn how to use the Text Analysis API to analyze and extract insights from your content. The API supports text input, file uploads, and URL-based content processing.

Base URL

url

https://api.apicodex.io/text-analysis

Authentication

Authenticate every request with your API key: send it in an X-Api-Key header, or pass it as an ?apikey= query parameter.

headers

{
"X-Api-Key": "YOUR_API_KEY"
}

Request and response

Illustrative values. Confirm current endpoint behavior, quotas, and data freshness in the API documentation before production use.

request · http

POST /text-analysis/v1/analyze/text HTTP/1.1
Host: api.apicodex.io
Content-Type: application/json
X-Api-Key: YOUR_API_KEY

{
  "text": "Apple Inc. announced new products in California yesterday.",
  "analysis": ["patterns", "entities", "sentiment", "language"]
}

response · json

{
  "success": true,
  "language": { "detected": "en", "confidence": 0.99 },
  "sentiment": { "score": 0.65, "label": "positive" },
  "entities": [
    { "type": "ORG", "value": "Apple Inc.", "start": 0, "end": 10 },
    { "type": "LOCATION", "value": "California", "start": 39, "end": 49 },
    { "type": "DATE", "value": "yesterday", "start": 50, "end": 59 }
  ],
  "patterns": { "emails": [], "urls": [], "dates": ["yesterday"] }
}

Code examples

JavaScript/Node.js

const axios = require('axios');

const options = {
  method: 'POST',
  url: 'https://api.apicodex.io/text-analysis/v1/analyze/text',
  headers: {
    'content-type': 'application/json',
    'X-Api-Key': 'YOUR_API_KEY'
  },
  data: {
    text: 'Apple Inc. announced new products in California yesterday.',
    analysis: ['patterns', 'entities', 'sentiment', 'language']
  }
};

try {
  const response = await axios.request(options);
  console.log(response.data);
} catch (error) {
  console.error(error);
}

Python

import requests

url = "https://api.apicodex.io/text-analysis/v1/analyze/text"

payload = {
    "text": "Apple Inc. announced new products in California yesterday.",
    "analysis": ["patterns", "entities", "sentiment", "language"]
}

headers = {
    "content-type": "application/json",
    "X-Api-Key": "YOUR_API_KEY"
}

response = requests.post(url, json=payload, headers=headers)

print(response.json())

cURL

curl --request POST \
  --url https://api.apicodex.io/text-analysis/v1/analyze/text \
  --header 'content-type: application/json' \
  --header 'X-Api-Key: YOUR_API_KEY' \
  --data '{
    "text": "Apple Inc. announced new products in California yesterday.",
    "analysis": ["patterns", "entities", "sentiment", "language"]
  }'

Endpoints

POST /v1/analyze/text

Send the text as JSON {"text": "..."}, as text/plain, or as a multipart text field.

POST /v1/analyze/upload

Upload a PDF, HTML or plain-text file (multipart/form-data, field name: file).

curl -X POST "https://api.apicodex.io/text-analysis/v1/analyze/upload" -H "X-Api-Key: YOUR_API_KEY" -F "[email protected]"

POST /v1/analyze/url

Fetch a public URL and process its text. JSON body: {"url": "https://..."}.

Analysis Types

The API supports the following types of analysis:

patterns Detect common text patterns

entities Named entity recognition

sentiment Text sentiment analysis

language Language detection

topics Topic extraction

keywords Keyword extraction

summary Text summarization

moderation Content moderation

custom Custom analysis rules

Pricing

Text Analysis API Pricing

This API costs 25 credits per request. Credits are shared across every API on your plan.

Estimate your monthly cost

Drag the slider to match your expected request volume. We'll recommend the cheapest plan that covers it.

Requests per month 10,000

Recommended plan

Starter

$29.99 /month

Credits used

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Cost per 1K requests

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Free

1,000 credits · ~40 requests/mo · 2 rps

All APIs included
No credit card required
Full documentation and examples
MCP server for your AI agents
Up to 5 active API keys

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Starter

Popular

$29.99 /month

50,000 credits · ~2,000 requests/mo · 10 rps

All APIs included
Email support
Usage dashboard and analytics
MCP server for your AI agents
Up to 5 active API keys

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Pro

$99.99 /month

250,000 credits · ~10,000 requests/mo · 25 rps

All APIs included
Priority support
MCP server for your AI agents
Up to 5 active API keys

Start with Pro

FAQ

Text Analysis API

Key features

Built for

Hand the Text Analysis API to your agent

Find themes in customer feedback

Summarize a competitor's page

Moderate community posts

Build the request for your data

Text Analysis API Documentation

Base URL

Authentication

Request and response

Code examples

Endpoints

POST /v1/analyze/text

POST /v1/analyze/upload

POST /v1/analyze/url

Analysis Types

Text Analysis API Pricing

Free

Starter

Pro

Need a different plan?

Frequently asked questions

Related APIs