Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
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A. B. C. D.D
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processingThe most appropriate AI workload for detecting when a customer is upset based on what they type in the chatbot is natural language processing (NLP).
Natural language processing is a branch of AI that deals with the interaction between computers and humans using natural language. It involves teaching machines to understand and interpret human language in a way that they can analyze, process, and generate responses that are similar to human language.
In this scenario, the chatbot needs to analyze the customer's input and determine whether they are upset or not based on the language they use. NLP can help with this by analyzing the text for tone, sentiment, and other emotional indicators.
Anomaly detection involves identifying unusual patterns or behavior in data that deviate from what is expected. This may not be the best choice for detecting customer upset in chatbot conversations.
Computer vision is the field of AI that deals with teaching machines to interpret and understand visual information from images or videos. It is not relevant in this scenario.
Regression is a statistical method used to estimate relationships between variables. It may not be the best approach for detecting customer sentiment in chatbot conversations.
Therefore, the most appropriate AI workload for detecting when a customer is upset based on what they type in the chatbot is natural language processing.