AI Functions / Text Anaytics

You can replace the retired Power BI Text Analytics by calling Azure AI Language (Text Analytics) APIs from Power Query or Fabric. Below are the official Microsoft docs:
Azure AI Language – Overview
https://learn.microsoft.com/azure/ai-services/language-service/overview
Language Detection
https://learn.microsoft.com/azure/ai-services/language-service/language-detection/overview
Sentiment Analysis
https://learn.microsoft.com/azure/ai-services/language-service/sentiment-opinion-mining/overview
Key Phrase Extraction
https://learn.microsoft.com/azure/ai-services/language-service/key-phrase-extraction/overview
Calling REST APIs from Power Query (Web.Contents)
https://learn.microsoft.com/power-query/connectors/web/web
https://learn.microsoft.com/powerquery-m/web-contents
Microsoft Fabric Notebooks (recommended for large volumes)
https://learn.microsoft.com/fabric/data-engineering/notebooks

Hi  MJEnnis ,

Thanks for the detailed follow up and for explaining what you have already done. Given the pressure you were under, your approach of preserving the historical text analytics results and restoring refresh stability was absolutely the right call, and it is exactly what many customers ended up doing to unblock dependent reports and processes.

Since you are now at the decision point for handling future data, below are all the official Microsoft documents you need, collected in one place and grouped by approach so you can decide what fits best. I am listing them point wise as requested so they are easy to scan and refer back to.

Power BI and Power Query approach using Azure AI Language
This path is closest to how the old AI Functions worked and is usually the fastest to adopt if you are already comfortable with Power Query.

Azure AI Language service overview
https://learn.microsoft.com/azure/ai-services/language-service/overview

Language detection documentation
https://learn.microsoft.com/azure/ai-services/language-service/language-detection/overview

Sentiment analysis and opinion mining
https://learn.microsoft.com/azure/ai-services/language-service/sentiment-opinion-mining/overview

Key phrase extraction
https://learn.microsoft.com/azure/ai-services/language-service/key-phrase-extraction/overview

Calling REST APIs from Power Query using Web.Contents
https://learn.microsoft.com/power-query/connectors/web/web
https://learn.microsoft.com/powerquery-m/web-contents

Microsoft Fabric approach recommended for larger volumes and future scale
This option is better if you expect high comment volumes or want a more robust long term setup. Fabric notebooks handle batching and API limits more gracefully than Power Query.

Azure AI Language pricing
https://azure.microsoft.com/pricing/details/cognitive-services/language-service/

From a practical standpoint, many users start by implementing the API calls in Power Query so they can keep everything inside the existing PBIX and then move the logic to Fabric later if volumes or refresh times become an issue. Since you already separated historical and future data, you are in a very good position to do this incrementally without disrupting reports again.

Hopefully having all the documentation in one place makes the next steps feel much more manageable. If you run into questions while choosing between Desktop and Fabric, feel free to ask.

Thank you.