According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore natural language processing (NLP) in Azure”, key phrase extraction is a core feature of the Azure AI Language Service that enables you to automatically identify the most important ideas or topics discussed in a body of text.
When analyzing customer feedback surveys, key phrase extraction helps summarize the main talking points or recurring themes by detecting significant words and phrases. For instance, if multiple customers write comments like “The checkout process is slow” or “Website speed could be improved,” the model may extract key phrases such as “checkout process” and “website speed.” This allows businesses to quickly understand the most common subjects without manually reading each response.
Let’s review the other options:
A. Language detection: Determines the language of the text (e.g., English, French, or Spanish) but does not identify main ideas.
B. Translation: Converts text from one language to another using Azure Translator; it does not summarize or extract key information.
C. Entity recognition: Identifies named entities such as people, organizations, locations, or dates. While useful for identifying specific details, it does not capture general topics or overall discussion points.
Therefore, the appropriate NLP feature for identifying main topics or themes within textual data such as survey responses is Key Phrase Extraction.
This capability is part of the Azure AI Language Service and is commonly used in sentiment analysis pipelines, customer feedback analytics, and business intelligence workflows to summarize large text datasets efficiently.
