<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT -->
---
name: azure-ai-textanalytics-py
description: "Azure AI Text Analytics SDK for Python"
tags: [ai, azure]
---

# Azure AI Text Analytics SDK for Python

Client library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more.

## Installation

```bash
pip install azure-ai-textanalytics
```

## Environment Variables

```bash
AZURE_LANGUAGE_ENDPOINT=https://<resource>.cognitiveservices.azure.com
AZURE_LANGUAGE_KEY=<your-api-key>  # If using API key
```

## Authentication

### API Key

```python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
key = os.environ["AZURE_LANGUAGE_KEY"]

client = TextAnalyticsClient(endpoint, AzureKeyCredential(key))
```

### Entra ID (Recommended)

```python
from azure.ai.textanalytics import TextAnalyticsClient
from azure.identity import DefaultAzureCredential

client = TextAnalyticsClient(
    endpoint=os.environ["AZURE_LANGUAGE_ENDPOINT"],
    credential=DefaultAzureCredential()
)
```

## Sentiment Analysis

```python
documents = [
    "I had a wonderful trip to Seattle last week!",
    "The food was terrible and the service was slow."
]

result = client.analyze_sentiment(documents, show_opinion_mining=True)

for doc in result:
    if not doc.is_error:
        print(f"Sentiment: {doc.sentiment}")
        print(f"Scores: pos={doc.confidence_scores.positive:.2f}, "
              f"neg={doc.confidence_scores.negative:.2f}, "
              f"neu={doc.confidence_scores.neutral:.2f}")
        
        # Opinion mining (aspect-based sentiment)
        for sentence in doc.sentences:
            for opinion in sentence.mined_opinions:
                target = opinion.target
                print(f"  Target: '{target.text}' - {target.sentiment}")
                for assessment in opinion.assessments:
                    print(f"    Assessment: '{assessment.text}' - {assessment.sentiment}")
```

## Entity Recognition

```python
documents = ["Microsoft was founded by Bill Gates and Paul Allen in Albuquerque."]

result = client.recognize_entities(documents)

for doc in result:
    if not doc.is_error:
        for entity in doc.entities:
            print(f"Entity: {entity.text}")
            print(f"  Category: {entity.category}")
            print(f"  Subcategory: {entity.subcategory}")
            print(f"  Confidence: {entity.confidence_score:.2f}")
```

## PII Detection

```python
documents = ["My SSN is 123-45-6789 and my email is john@example.com"]

result = client.recognize_pii_entities(documents)

for doc in result:
    if not doc.is_error:
        print(f"Redacted: {doc.redacted_text}")
        for entity in doc.entities:
            print(f"PII: {entity.text} ({entity.category})")
```

## Key Phrase Extraction

```python
documents = ["Azure AI provides powerful machine learning capabilities for developers."]

result = client.extract_key_phrases(documents)

for doc in result:
    if not doc.is_error:
        print(f"Key phrases: {doc.key_phrases}")
```

## Language Detection

```python
documents = ["Ce document est en francais.", "This is written in English."]

result = client.detect_language(documents)

for doc in result:
    if not doc.is_error:
        print(f"Language: {doc.primary_language.name} ({doc.primary_language.iso6391_name})")
        print(f"Confidence: {doc.primary_language.confidence_score:.2f}")
```

## Healthcare Text Analytics

```python
documents = ["Patient has diabetes and was prescribed metformin 500mg twice daily."]

poller = client.begin_analyze_healthcare_entities(documents)
result = poller.result()

for doc in result:
    if not doc.is_error:
        for entity in doc.entities:
            print(f"Entity: {entity.text}")
            print(f"  Category: {entity.category}")
            print(f"  Normalized: {entity.normalized_text}")
            
            # Entity links (UMLS, etc.)
            for link in entity.data_sources:
                print(f"  Link: {link.name} - {link.entity_id}")
```

## Multiple Analysis (Batch)

```python
from azure.ai.textanalytics import (
    RecognizeEntitiesAction,
    ExtractKeyPhrasesAction,
    AnalyzeSentimentAction
)

documents = ["Microsoft announced new Azure AI features at Build conference."]

poller = client.begin_analyze_actions(
    documents,
    actions=[
        RecognizeEntitiesAction(),
        ExtractKeyPhrasesAction(),
        AnalyzeSentimentAction()
    ]
)

results = poller.result()
for doc_results in results:
    for result in doc_results:
        if result.kind == "EntityRecognition":
            print(f"Entities: {[e.text for e in result.entities]}")
        elif result.kind == "KeyPhraseExtraction":
            print(f"Key phrases: {result.key_phrases}")
        elif result.kind == "SentimentAnalysis":
            print(f"Sentiment: {result.sentiment}")
```

## Async Client

```python
from azure.ai.textanalytics.aio import TextAnalyticsClient
from azure.identity.aio import DefaultAzureCredential

async def analyze():
    async with TextAnalyticsClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        result = await client.analyze_sentiment(documents)
        # Process results...
```

## Client Types

| Client | Purpose |
|--------|---------|
| `TextAnalyticsClient` | All text analytics operations |
| `TextAnalyticsClient` (aio) | Async version |

## Available Operations

| Method | Description |
|--------|-------------|
| `analyze_sentiment` | Sentiment analysis with opinion mining |
| `recognize_entities` | Named entity recognition |
| `recognize_pii_entities` | PII detection and redaction |
| `recognize_linked_entities` | Entity linking to Wikipedia |
| `extract_key_phrases` | Key phrase extraction |
| `detect_language` | Language detection |
| `begin_analyze_healthcare_entities` | Healthcare NLP (long-running) |
| `begin_analyze_actions` | Multiple analyses in batch |

## Best Practices

1. **Use batch operations** for multiple documents (up to 10 per request)
2. **Enable opinion mining** for detailed aspect-based sentiment
3. **Use async client** for high-throughput scenarios
4. **Handle document errors** — results list may contain errors for some docs
5. **Specify language** when known to improve accuracy
6. **Use context manager** or close client explicitly

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