Struggling with Azure AI Engineer Associate AI-102 exam scenarios? Learn how Azure AI Language analyzes mixed sentiment in customer reviews, balances conflicting feedback scores, and tackles certification questions with real-world examples. Master sentiment analysis for exam success.
Table of Contents
Question
You are an AI application developer. Your organization, Nutex Inc., provides e-commerce services. You are developing a customer feedback analysis tool to gauge customer sentiment based on reviews and comments left on the organization’s e-commerce platform.
You have implemented Azure AI Language to determine the sentiment of the feedback.
You receive the following customer review:
“I had a great experience with the product, but the delivery was delayed and the customer service was not helpful.”
What sentiment score would the Azure AI Language service most likely assign to this review, and how would it categorize the sentiment?
A. Positive sentiment with a high sentiment score
B. Negative sentiment with a high sentiment score
C. Neutral sentiment with a low sentiment score
D. Mixed sentiment with a balanced sentiment score
Answer
D. Mixed sentiment with a balanced sentiment score
Explanation
The Azure AI Language service would most likely assign the mixed sentiment category with a balanced sentiment score to this review. This option is the most accurate as the review contains both positive and negative feedback. The customer is happy with the product but dissatisfied with the delivery and customer service. Mixed sentiment best describes the overall tone of the review, considering both positive and negative comments. The sentiment score then balances these contrasting views.
When analyzing sentiment in text using tools such as Azure AI Language, the text is categorized into three main sentiment labels: Positive, Negative, and Neutral. In some cases, a Mixed sentiment label may also be used when a piece of text expresses both positive and negative sentiments.
The Azure AI Language service would not assign a positive sentiment with a high sentiment score in the given scenario. A positive sentiment indicates that the text expresses favorable opinions, emotions, or attitudes. Words and phrases that convey happiness, satisfaction, approval, or praise are typically associated with positive sentiment. This label is useful for identifying customer feedback or reviews that are generally favorable and can be used to highlight strengths or successful experiences.
The Azure AI Language service would not assign a negative sentiment with a high sentiment score in the given scenario. A negative sentiment indicates that the text expresses unfavorable opinions, emotions, or attitudes. Words and phrases that convey dissatisfaction, frustration, disapproval, or criticism are typically associated with negative sentiment.
The Azure AI Language service would not assign a neutral sentiment with a low sentiment score in the given scenario. A neutral sentiment indicates that the text is objective or factual and does not express strong positive or negative emotions. This label is often assigned to statements that provide information without conveying an opinion. A neutral sentiment is used for content that is informative or factual, such as announcements or descriptions without any emotional tone.
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