Azure AI 文本分析 Python SDK

azure-ai-textanalytics-py
分类数据
作者Agentic Awesome Skills 社区
许可MIT
评分4.60/5
使用16.4K

Azure AI Text Analytics Python SDK

Azure AI Language 服务 NLP 功能的客户端库，包括情感分析、实体识别、关键短语等。

安装

bash
pip install azure-ai-textanalytics

环境变量

bash
AZURE_LANGUAGE_ENDPOINT=https://<resource>.cognitiveservices.azure.com
AZURE_LANGUAGE_KEY=<your-api-key>  # 如果使用 API 密钥

身份验证

API 密钥

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 (推荐)

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

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

情感分析

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}")
        
        # 观点挖掘 (基于方面的情感分析)
        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}")

实体识别

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 检测

python
documents = ["My SSN is 123-45-6789 and my email is [email protected]"]

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})")

关键短语提取

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}")

语言检测

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}")
code
## 医疗文本分析
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}")

# 实体链接 (UMLS 等)
for link in entity.data_sources:
print(f" Link: {link.name} - {link.entity_id}")

code
## 多重分析（批处理）
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}")

code
## 异步客户端
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)
# 处理结果...
``

客户端类型

| 客户端 | 用途 |
|--------|---------|
| TextAnalyticsClient | 所有文本分析操作 |
| TextAnalyticsClient (aio) | 异步版本 |

可用操作

| 方法 | 描述 |
|--------|-------------|
| analyze_sentiment | 包含观点挖掘的情感分析 |
| recognize_entities | 命名实体识别 |
| recognize_pii_entities | PII（个人可识别信息）检测与脱敏 |
| recognize_linked_entities | 实体链接至 Wikipedia |
| extract_key_phrases | 关键短语提取 |
| detect_language | 语言检测 |
| begin_analyze_healthcare_entities | 医疗 NLP（长耗时操作） |
| begin_analyze_actions` | 批处理执行多项分析 |

最佳实践

1. 使用批处理操作处理多个文档（每次请求最多 10 个）
2. 启用观点挖掘以获取详细的基于方面的 sentiment 分析
3. 使用异步客户端以应对高吞吐量场景
4. 处理文档错误 —— 结果列表中可能包含部分文档的错误信息
5. 在已知语言时指定语言以提高准确率
6. 使用上下文管理器或显式关闭客户端

适用场景

本技能适用于执行概览中所描述的工作流或操作。

局限性

  • 仅在任务明确符合上述范围时使用此技能。
  • 不要将输出结果视为环境特定验证、测试或专家评审的替代方案。
  • 如果缺失必要的输入、权限、安全边界或成功标准，请停止并请求澄清。