An airline's customer service team receives thousands of typed chat messages daily and wants to automatically flag messages expressing frustration or anger so agents can prioritize them. Which AI capability best fits this requirement?
Select an answer to reveal the explanation.
Short Explanation
You need to know how the customer feels, not just what the message contains. Sentiment analysis scores emotional tone, so angry or frustrated chats can jump the queue. The other capabilities help with details, length, or language, but they don't measure mood.
Full Explanation
Sentiment analysis evaluates text to determine whether the expressed tone is positive, negative, or neutral, and it can be tuned to flag negative or angry language, which is precisely the prioritization signal the airline wants. Named entity recognition is incorrect because it extracts structured items such as flight numbers, dates, or locations from text, which could help route a ticket but says nothing about the customer's emotional state. Text summarization is incorrect because it shortens content for readability but does not classify or score tone, so a calm long message and an angry long message could summarize similarly without revealing which one is urgent. Language detection is incorrect because it only identifies which language is being used, a preprocessing step that has no bearing on whether the message conveys frustration.