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Use for natural language processing on text.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\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],"azure ai","azure","azure ai textanalytics py","detection","natural language","language","community","use","text","healthcare","analysis","pii","nlp","sentiment","sentiment analysis","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":51,"source":35,"date_added":52,"description":7},"unknown","2026-02-27","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fazure-ai-textanalytics-py",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill azure-ai-textanalytics-py",{"tool":67,"command":68},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill azure-ai-textanalytics-py",{"tool":70,"command":71},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill azure-ai-textanalytics-py","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fazure-ai-textanalytics-py\u002FSKILL.md",true,[75],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"evals\u002Ftriggers.json","REFERENCE","参考文档",3457,"https:\u002F\u002Fgithub.com\u002Fevent4u-app\u002Fagent-config\u002Fblob\u002Fmain\u002Fsrc\u002Fskills\u002Fcontext-authoring\u002Fevals\u002Ftriggers.json",[82,93,109,136,155,182],{"skillNo":83,"name":84,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":90,"publishedAt":46},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[87],{"categoryNo":88,"categoryName":89,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[91,92],"Test Skill","test",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":46},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[100,101,102,103,104,105,106,35,107,108],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":118,"publishedAt":46},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). 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