You are developing an app that will perform a sentiment analysis of social media posts by using the Azure AI Language service. You perform a test on a sample post. You need to quantify the results…

Question

Options

• AconfidenceThreshold
• Bsentiment
• CconfidenceScores
• Drelations

• A
  8% (3)
• B
  15% (6)
• C
  72% (28)
• D
  5% (2)

Explanation

Sentiment analysis The sentiment analysis feature assigns sentiment labels, such as "negative," "neutral," and "positive." The service determines these labels using the highest confidence score. Sentiment is evaluated at both the sentence level and the document level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral, and negative sentiment. In Azure AI Language's sentiment analysis, confidenceScores are numerical values between 0 and 1 that represent the probability that the text belongs to a specific sentiment (positive, neutral, or negative). A score closer to 1 indicates a higher confidence from the service that the text exhibits that sentiment, while a lower score signifies less confidence in that particular label. The service calculates these scores for both individual sentences and the entire document, providing a granular understanding of sentiment. How to interpret confidenceScores: High Score (close to 1): The model is very sure about the assigned sentiment. For example, a positive score of 0.95 means the model is 95% confident the text is positive. Low Score (close to 0): The model is not very sure about the assigned sentiment. Scores for each sentiment: For any given piece of text, the service returns a score for positive, neutral, and negative sentiment. The sentiment label that receives the highest score is assigned as the overall sentiment for that text. https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/ov

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