Azure AI Text Analytics SDK for Python Client library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more. Installation pip install azure-ai-textanalytics Environment Variables AZURE_LANGUAGE_ENDPOINT = https:// < resource

.cognitiveservices.azure.com AZURE_LANGUAGE_KEY = < your-api-key

If using API key

Authentication API Key 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) from azure . ai . textanalytics import TextAnalyticsClient from azure . identity import DefaultAzureCredential client = TextAnalyticsClient ( endpoint = os . environ [ "AZURE_LANGUAGE_ENDPOINT" ] , credential = DefaultAzureCredential ( ) ) Sentiment Analysis 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 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 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 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 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 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) 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 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 Use batch operations for multiple documents (up to 10 per request) Enable opinion mining for detailed aspect-based sentiment Use async client for high-throughput scenarios Handle document errors — results list may contain errors for some docs Specify language when known to improve accuracy Use context manager or close client explicitly When to Use This skill is applicable to execute the workflow or actions described in the overview.