According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn module “Explore natural language processing”, NLP enables computers to understand, interpret, and analyze human language. One of its key capabilities is sentiment analysis, which detects emotional tone (positive, negative, or neutral) in text.

In this scenario, the chatbot must detect when a customer is upset based on what they type. This directly maps to sentiment analysis, a core NLP function. The Text Analytics service within Azure Cognitive Services provides prebuilt sentiment analysis models that return a sentiment score and classification (e.g., positive, neutral, negative). As per Microsoft Learn, “Natural language processing allows systems to understand sentiment and intent within text and speech to derive meaningful insights.”

Explanation of other options:

A. Anomaly detection identifies unusual patterns in data (e.g., fraud detection), not emotions in text.

B. Semantic segmentation is a computer vision technique used to label pixels in an image.

C. Regression predicts continuous numeric values and is not related to understanding text or emotions.

Therefore, to enable the chatbot to detect when a user is upset based on text input, the correct AI workload is Natural Language Processing (NLP), specifically through Azure Text Analytics sentiment analysis.