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Patkik
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azure-ai-textanalytics-py

Provide a Python client for Azure Text Analytics NLP tasks.

Updated Mar 30, 2026
Search Tags:#azure#nlp#language-detection#sentiment-analysis#text-analytics#entity-recognition#key-phrases
One-click install
npx skills add https://github.com/Patkik/Multi-tenant-SaaS-Catering-V2 --skill azure-ai-textanalytics-py-patkik
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-ai-textanalytics-py
Source: https://github.com/Patkik/Multi-tenant-SaaS-Catering-V2/tree/main/.agents/skills/azure-ai-textanalytics-py
Command: npx skills add https://github.com/Patkik/Multi-tenant-SaaS-Catering-V2 --skill azure-ai-textanalytics-py-patkik

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Azure AI Text Analytics SDK provides Python clients for performing NLP tasks such as sentiment analysis, entity recognition, key phrase extraction, language detection, PII detection, and healthcare NLP on text data.

Core Features & Use Cases

  • Sentiment analysis with optional opinion mining to extract sentiment per sentence
  • Entity recognition to identify people, organizations, locations, and more
  • Key phrase extraction to surface important phrases from documents
  • Language detection to determine primary language and probabilities across texts
  • PII detection and, where applicable, domain-restricted privacy safeguards
  • Healthcare text analytics for clinical entity extraction and relations
  • Use cases include analyzing customer feedback, parsing support tickets, and extracting insights from multilingual content

Quick Start

Install the azure-ai-textanalytics package and run a simple sentiment analysis example to verify setup.

Frequently Asked Questions about azure-ai-textanalytics-py

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform sentiment analysis and opinion mining on customer feedback in Python?▼

Sentiment analysis in Python uses the Azure Text Analytics client to evaluate text and extract sentiment scores per sentence. It supports optional opinion mining to break down sentiments by aspect and processes customer feedback in batches.

Can I detect languages and extract key phrases from multilingual documents at scale?▼

Language detection identifies the primary language and probabilities across multilingual texts, while key phrase extraction surfaces important phrases from documents. Both run via the Python client, supporting batch processing for analyzing content at scale.

Does Azure Text Analytics support PII detection and healthcare NLP for document processing?▼

Azure Text Analytics supports PII detection to identify sensitive information and domain-restricted privacy safeguards in text. It also provides healthcare NLP to extract clinical entities and relations, enabling clinical document processing.

What is the best way to authenticate and handle errors for NLP tasks in Python?▼

The best way to authenticate and handle NLP errors is using deterministic patterns in the Azure Text Analytics client. It supports credential-based authentication, comprehensive error handling, and both synchronous and asynchronous processing.

How do I extract entities like people and organizations from support tickets?▼

Entity recognition identifies people, organizations, locations, and more from text data like support tickets. The Python client processes text to extract and categorize named entities, enabling developers to parse and extract insights from documents.

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