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Use for natural language processing on text.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcursor\u002Fazure-ai-textanalytics-py\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"azure ai","azure","azure ai textanalytics py","healthcare","2026","unknown","for","processing","entity recognition","natural language","community","use","text","nlp","analysis","pii","detection","sentiment","analytics","sdk","2026-11-10 10:12:30",false,"---\nname: azure-ai-textanalytics-py\ndescription: 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.\nrisk: unknown\nsource: community\ndate_added: '2026-02-27'\n---\n\n# Azure AI Text Analytics SDK for Python\n\nClient library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more.\n\n## Installation\n\n```bash\npip install azure-ai-textanalytics\n```\n\n## Environment Variables\n\n```bash\nAZURE_LANGUAGE_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com\nAZURE_LANGUAGE_KEY=\u003Cyour-api-key>  # If using API key\n```\n\n## Authentication\n\n### API Key\n\n```python\nimport os\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.textanalytics import TextAnalyticsClient\n\nendpoint = os.environ[\"AZURE_LANGUAGE_ENDPOINT\"]\nkey = os.environ[\"AZURE_LANGUAGE_KEY\"]\n\nclient = TextAnalyticsClient(endpoint, AzureKeyCredential(key))\n```\n\n### Entra ID (Recommended)\n\n```python\nfrom azure.ai.textanalytics import TextAnalyticsClient\nfrom azure.identity import DefaultAzureCredential\n\nclient = TextAnalyticsClient(\n    endpoint=os.environ[\"AZURE_LANGUAGE_ENDPOINT\"],\n    credential=DefaultAzureCredential()\n)\n```\n\n## Sentiment Analysis\n\n```python\ndocuments = [\n    \"I had a wonderful trip to Seattle last week!\",\n    \"The food was terrible and the service was slow.\"\n]\n\nresult = client.analyze_sentiment(documents, show_opinion_mining=True)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Sentiment: {doc.sentiment}\")\n        print(f\"Scores: pos={doc.confidence_scores.positive:.2f}, \"\n              f\"neg={doc.confidence_scores.negative:.2f}, \"\n              f\"neu={doc.confidence_scores.neutral:.2f}\")\n        \n        # Opinion mining (aspect-based sentiment)\n        for sentence in doc.sentences:\n            for opinion in sentence.mined_opinions:\n                target = opinion.target\n                print(f\"  Target: '{target.text}' - {target.sentiment}\")\n                for assessment in opinion.assessments:\n                    print(f\"    Assessment: '{assessment.text}' - {assessment.sentiment}\")\n```\n\n## Entity Recognition\n\n```python\ndocuments = [\"Microsoft was founded by Bill Gates and Paul Allen in Albuquerque.\"]\n\nresult = client.recognize_entities(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        for entity in doc.entities:\n            print(f\"Entity: {entity.text}\")\n            print(f\"  Category: {entity.category}\")\n            print(f\"  Subcategory: {entity.subcategory}\")\n            print(f\"  Confidence: {entity.confidence_score:.2f}\")\n```\n\n## PII Detection\n\n```python\ndocuments = [\"My SSN is 123-45-6789 and my email is john@example.com\"]\n\nresult = client.recognize_pii_entities(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Redacted: {doc.redacted_text}\")\n        for entity in doc.entities:\n            print(f\"PII: {entity.text} ({entity.category})\")\n```\n\n## Key Phrase Extraction\n\n```python\ndocuments = [\"Azure AI provides powerful machine learning capabilities for developers.\"]\n\nresult = client.extract_key_phrases(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Key phrases: {doc.key_phrases}\")\n```\n\n## Language Detection\n\n```python\ndocuments = [\"Ce document est en francais.\", \"This is written in English.\"]\n\nresult = client.detect_language(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Language: {doc.primary_language.name} ({doc.primary_language.iso6391_name})\")\n        print(f\"Confidence: {doc.primary_language.confidence_score:.2f}\")\n```\n\n## Healthcare Text Analytics\n\n```python\ndocuments = [\"Patient has diabetes and was prescribed metformin 500mg twice daily.\"]\n\npoller = client.begin_analyze_healthcare_entities(documents)\nresult = poller.result()\n\nfor doc in result:\n    if not doc.is_error:\n        for entity in doc.entities:\n            print(f\"Entity: {entity.text}\")\n            print(f\"  Category: {entity.category}\")\n            print(f\"  Normalized: {entity.normalized_text}\")\n            \n            # Entity links (UMLS, etc.)\n            for link in entity.data_sources:\n                print(f\"  Link: {link.name} - {link.entity_id}\")\n```\n\n## Multiple Analysis (Batch)\n\n```python\nfrom azure.ai.textanalytics import (\n    RecognizeEntitiesAction,\n    ExtractKeyPhrasesAction,\n    AnalyzeSentimentAction\n)\n\ndocuments = [\"Microsoft announced new Azure AI features at Build conference.\"]\n\npoller = client.begin_analyze_actions(\n    documents,\n    actions=[\n        RecognizeEntitiesAction(),\n        ExtractKeyPhrasesAction(),\n        AnalyzeSentimentAction()\n    ]\n)\n\nresults = poller.result()\nfor doc_results in results:\n    for result in doc_results:\n        if result.kind == \"EntityRecognition\":\n            print(f\"Entities: {[e.text for e in result.entities]}\")\n        elif result.kind == \"KeyPhraseExtraction\":\n            print(f\"Key phrases: {result.key_phrases}\")\n        elif result.kind == \"SentimentAnalysis\":\n            print(f\"Sentiment: {result.sentiment}\")\n```\n\n## Async Client\n\n```python\nfrom azure.ai.textanalytics.aio import TextAnalyticsClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def analyze():\n    async with TextAnalyticsClient(\n        endpoint=endpoint,\n        credential=DefaultAzureCredential()\n    ) as client:\n        result = await client.analyze_sentiment(documents)\n        # Process results...\n```\n\n## Client Types\n\n| Client | Purpose |\n|--------|---------|\n| `TextAnalyticsClient` | All text analytics operations |\n| `TextAnalyticsClient` (aio) | Async version |\n\n## Available Operations\n\n| Method | Description |\n|--------|-------------|\n| `analyze_sentiment` | Sentiment analysis with opinion mining |\n| `recognize_entities` | Named entity recognition |\n| `recognize_pii_entities` | PII detection and redaction |\n| `recognize_linked_entities` | Entity linking to Wikipedia |\n| `extract_key_phrases` | Key phrase extraction |\n| `detect_language` | Language detection |\n| `begin_analyze_healthcare_entities` | Healthcare NLP (long-running) |\n| `begin_analyze_actions` | Multiple analyses in batch |\n\n## Best Practices\n\n1. **Use batch operations** for multiple documents (up to 10 per request)\n2. **Enable opinion mining** for detailed aspect-based sentiment\n3. **Use async client** for high-throughput scenarios\n4. **Handle document errors** — results list may contain errors for some docs\n5. **Specify language** when known to improve accuracy\n6. **Use context manager** or close client explicitly\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n","# Azure AI Text Analytics SDK for Python\n\nClient library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more.\n\n## Installation\n\n```bash\npip install azure-ai-textanalytics\n```\n\n## Environment Variables\n\n```bash\nAZURE_LANGUAGE_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com\nAZURE_LANGUAGE_KEY=\u003Cyour-api-key>  # If using API key\n```\n\n## Authentication\n\n### API Key\n\n```python\nimport os\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.textanalytics import TextAnalyticsClient\n\nendpoint = os.environ[\"AZURE_LANGUAGE_ENDPOINT\"]\nkey = os.environ[\"AZURE_LANGUAGE_KEY\"]\n\nclient = TextAnalyticsClient(endpoint, AzureKeyCredential(key))\n```\n\n### Entra ID (Recommended)\n\n```python\nfrom azure.ai.textanalytics import TextAnalyticsClient\nfrom azure.identity import DefaultAzureCredential\n\nclient = TextAnalyticsClient(\n    endpoint=os.environ[\"AZURE_LANGUAGE_ENDPOINT\"],\n    credential=DefaultAzureCredential()\n)\n```\n\n## Sentiment Analysis\n\n```python\ndocuments = [\n    \"I had a wonderful trip to Seattle last week!\",\n    \"The food was terrible and the service was slow.\"\n]\n\nresult = client.analyze_sentiment(documents, show_opinion_mining=True)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Sentiment: {doc.sentiment}\")\n        print(f\"Scores: pos={doc.confidence_scores.positive:.2f}, \"\n              f\"neg={doc.confidence_scores.negative:.2f}, \"\n              f\"neu={doc.confidence_scores.neutral:.2f}\")\n        \n        # Opinion mining (aspect-based sentiment)\n        for sentence in doc.sentences:\n            for opinion in sentence.mined_opinions:\n                target = opinion.target\n                print(f\"  Target: '{target.text}' - {target.sentiment}\")\n                for assessment in opinion.assessments:\n                    print(f\"    Assessment: '{assessment.text}' - {assessment.sentiment}\")\n```\n\n## Entity Recognition\n\n```python\ndocuments = [\"Microsoft was founded by Bill Gates and Paul Allen in Albuquerque.\"]\n\nresult = client.recognize_entities(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        for entity in doc.entities:\n            print(f\"Entity: {entity.text}\")\n            print(f\"  Category: {entity.category}\")\n            print(f\"  Subcategory: {entity.subcategory}\")\n            print(f\"  Confidence: {entity.confidence_score:.2f}\")\n```\n\n## PII Detection\n\n```python\ndocuments = [\"My SSN is 123-45-6789 and my email is john@example.com\"]\n\nresult = client.recognize_pii_entities(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Redacted: {doc.redacted_text}\")\n        for entity in doc.entities:\n            print(f\"PII: {entity.text} ({entity.category})\")\n```\n\n## Key Phrase Extraction\n\n```python\ndocuments = [\"Azure AI provides powerful machine learning capabilities for developers.\"]\n\nresult = client.extract_key_phrases(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Key phrases: {doc.key_phrases}\")\n```\n\n## Language Detection\n\n```python\ndocuments = [\"Ce document est en francais.\", \"This is written in English.\"]\n\nresult = client.detect_language(documents)\n\nfor doc in result:\n    if not doc.is_error:\n        print(f\"Language: {doc.primary_language.name} ({doc.primary_language.iso6391_name})\")\n        print(f\"Confidence: {doc.primary_language.confidence_score:.2f}\")\n```\n\n## Healthcare Text Analytics\n\n```python\ndocuments = [\"Patient has diabetes and was prescribed metformin 500mg twice daily.\"]\n\npoller = client.begin_analyze_healthcare_entities(documents)\nresult = poller.result()\n\nfor doc in result:\n    if not doc.is_error:\n        for entity in doc.entities:\n            print(f\"Entity: {entity.text}\")\n            print(f\"  Category: {entity.category}\")\n            print(f\"  Normalized: {entity.normalized_text}\")\n            \n            # Entity links (UMLS, etc.)\n            for link in entity.data_sources:\n                print(f\"  Link: {link.name} - {link.entity_id}\")\n```\n\n## Multiple Analysis (Batch)\n\n```python\nfrom azure.ai.textanalytics import (\n    RecognizeEntitiesAction,\n    ExtractKeyPhrasesAction,\n    AnalyzeSentimentAction\n)\n\ndocuments = [\"Microsoft announced new Azure AI features at Build conference.\"]\n\npoller = client.begin_analyze_actions(\n    documents,\n    actions=[\n        RecognizeEntitiesAction(),\n        ExtractKeyPhrasesAction(),\n        AnalyzeSentimentAction()\n    ]\n)\n\nresults = poller.result()\nfor doc_results in results:\n    for result in doc_results:\n        if result.kind == \"EntityRecognition\":\n            print(f\"Entities: {[e.text for e in result.entities]}\")\n        elif result.kind == \"KeyPhraseExtraction\":\n            print(f\"Key phrases: {result.key_phrases}\")\n        elif result.kind == \"SentimentAnalysis\":\n            print(f\"Sentiment: {result.sentiment}\")\n```\n\n## Async Client\n\n```python\nfrom azure.ai.textanalytics.aio import TextAnalyticsClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def analyze():\n    async with TextAnalyticsClient(\n        endpoint=endpoint,\n        credential=DefaultAzureCredential()\n    ) as client:\n        result = await client.analyze_sentiment(documents)\n        # Process results...\n```\n\n## Client Types\n\n| Client | Purpose |\n|--------|---------|\n| `TextAnalyticsClient` | All text analytics operations |\n| `TextAnalyticsClient` (aio) | Async version |\n\n## Available Operations\n\n| Method | Description |\n|--------|-------------|\n| `analyze_sentiment` | Sentiment analysis with opinion mining |\n| `recognize_entities` | Named entity recognition |\n| `recognize_pii_entities` | PII detection and redaction |\n| `recognize_linked_entities` | Entity linking to Wikipedia |\n| `extract_key_phrases` | Key phrase extraction |\n| `detect_language` | Language detection |\n| `begin_analyze_healthcare_entities` | Healthcare NLP (long-running) |\n| `begin_analyze_actions` | Multiple analyses in batch |\n\n## Best Practices\n\n1. **Use batch operations** for multiple documents (up to 10 per request)\n2. **Enable opinion mining** for detailed aspect-based sentiment\n3. **Use async client** for high-throughput scenarios\n4. **Handle document errors** — results list may contain errors for some docs\n5. **Specify language** when known to improve accuracy\n6. **Use context manager** or close client explicitly\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n",{"name":5,"risk":34,"source":39,"date_added":54,"description":7},"2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fcursor\u002Fazure-ai-textanalytics-py","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill azure-ai-textanalytics-py",{"tool":67,"command":68},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill azure-ai-textanalytics-py",{"tool":70,"command":71},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill azure-ai-textanalytics-py","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcursor\u002Fazure-ai-textanalytics-py\u002FSKILL.md",true,[],[76,98,124,134,147,164],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":85,"publishedAt":49},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[82],{"categoryNo":83,"categoryName":84,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000037","律师与法务",[86,87,88,89,90,91,92,93,94,95,96,97],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch","with",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":49},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). 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