A retail company collects thousands of customer product reviews as free text and wants a single score for each review classifying the customer's overall tone as positive, negative, or neutral, without needing a list of specific topics or products mentioned. Which Azure AI Language capability should the team use?
Select an answer to reveal the explanation.
Short Explanation
Think of this as asking the model how the customer felt, not what they talked about. You need one tone verdict per review—positive, negative, or neutral—so sentiment analysis is the right Azure AI Language capability. Key phrases, named entities, and language detection tell you topics, mentions, or language, not attitude.
Full Explanation
The correct answer is sentiment analysis. Sentiment analysis is the Azure AI Language capability designed to evaluate free text and return a score or label indicating the overall positive, negative, mixed, or neutral tone of the passage, which is exactly what the retail team needs to summarize customer opinion at scale. Key phrase extraction is incorrect because it identifies the main topics or noun phrases in a passage, such as product names or complaint themes, but it does not judge whether the tone toward those topics is favorable or unfavorable. Named entity recognition is incorrect because it locates and categorizes specific items in text, such as people, organizations, locations, or dates, which would tell the team what is mentioned but not how the customer feels about it. Language detection is incorrect because it only identifies which natural language a piece of text is written in, a preliminary step that would be useful if reviews arrived in multiple languages but does nothing to measure sentiment on its own. Because the team's stated goal is a single tone score per review with no interest in itemizing topics, sentiment analysis is the capability that directly produces that output without extra processing.