sickn33/antigravity-awesome-skills/azure-ai-textanalytics-py

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Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

Install

npx vskill@latest install sickn33/antigravity-awesome-skills/azure-ai-textanalytics-py

SKILL.md

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()
)

Data handling

Calls send document text to Azure. Obtain authorization for private, PII or health data and verify region, retention and permitted use; redaction is probabilistic and does not guarantee de-identification. Healthcare extraction is not diagnosis. Examples are independent fragments; check each per-document error before consuming result fields.

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 category: {entity.category}") # Do not log detected sensitive values.

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

ClientPurpose
TextAnalyticsClientAll text analytics operations
TextAnalyticsClient (aio)Async version

Available Operations

MethodDescription
analyze_sentimentSentiment analysis with opinion mining
recognize_entitiesNamed entity recognition
recognize_pii_entitiesPII detection and redaction
recognize_linked_entitiesEntity linking to Wikipedia
extract_key_phrasesKey phrase extraction
detect_languageLanguage detection
begin_analyze_healthcare_entitiesHealthcare NLP (long-running)
begin_analyze_actionsMultiple 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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
26,818
Repository forks
4,552
Repository npm downloads, 7 days
5,562
7-day trend score
72

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 23 Mar 2026, 7 months ago.

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