What types of text can be classified using Azure AI Language?

The correct choice emphasizes the capabilities of Azure AI Language in analyzing and classifying text based on semantic understanding. Specifically, Azure AI Language can effectively identify sentiment, key phrases, and named entities within a piece of text. This means it can assess the emotional tone of the text, extract significant phrases that represent the core topic or message, and recognize specific entities such as names of people, organizations, or locations.

Sentiment analysis involves determining whether the text expresses a positive, negative, or neutral sentiment, which is invaluable for understanding user opinions or feedback. Key phrase extraction allows the identification of the most important themes or concepts within the text, facilitating summarization and deeper insights. Named entity recognition is crucial in processing unstructured data as it helps in recognizing relevant entities and categorizing them, enhancing data organization and retrieval.

In contrast, the other options focus on aspects that are either less about classification or not supported by Azure AI Language. Keywords and metadata, while important in information retrieval, don't capture the nuanced interpretations that Azure's NLP capabilities provide. Grammatical errors and punctuation pertain more to text quality checks rather than classification. Lastly, text formatting and style relate to the presentation of text, which does not align with Azure AI's analysis capabilities. Thus, the