Learn how to identify the appropriate AI workload for detecting customer sentiment in chatbot interactions. Prepare for the AI-900 Microsoft Azure AI Fundamentals certification exam with this detailed explanation of natural language processing and its application in sentiment analysis.
Table of Contents
Question
Your website use a chatbot to assist customers, and it need to detect when a customer is upset based on what the customer types when interacting with chatbot. Which type of Al workload should you use?
A. Computer vision
B. Anomaly detection
C. Natural language processing
D. Regression
Answer
C. Natural language processing
Explanation
To detect when a customer is upset based on their interactions with a chatbot on your website, the most suitable AI workload is natural language processing (NLP).
NLP is a branch of artificial intelligence that focuses on the interaction between computers and human language. It involves analyzing, understanding, and generating human language in a way that computers can process. NLP techniques enable machines to interpret the meaning, sentiment, and intent behind written or spoken language.
In the context of a chatbot, NLP can be used to analyze the text input provided by customers during their conversations. By applying sentiment analysis, a subfield of NLP, the chatbot can determine the emotional tone of the customer’s messages. Sentiment analysis algorithms are trained to classify text into different sentiment categories, such as positive, negative, or neutral, based on the words, phrases, and context used by the customer.
By leveraging NLP and sentiment analysis, the chatbot can identify when a customer is expressing frustration, anger, or dissatisfaction based on their language. This information can then be used to trigger appropriate actions, such as escalating the conversation to a human agent or providing personalized support to address the customer’s concerns.
The other options mentioned in the question are not directly relevant to detecting customer sentiment in chatbot interactions:
- Computer vision deals with enabling computers to interpret and understand visual information from the world, such as images and videos.
- Anomaly detection focuses on identifying unusual patterns or deviations from the norm in data.
- Regression is a statistical method used to predict a continuous numerical value based on input variables.
Therefore, the correct answer is C. Natural language processing, as it is the most appropriate AI workload for detecting customer sentiment in chatbot interactions.
Natural Language Processing (NLP) is a field of AI that focuses on the interaction between computers and humans through language. It’s ideal for understanding and analyzing text to detect emotions or sentiments.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.