Learn which natural language processing (NLP) technique is best for analyzing the sentiment of text on a positive to negative scale. Prepare for the Microsoft Azure AI Fundamentals AI-900 certification exam.
Table of Contents
Question
You need to map the right type of natural language processing workload for following scenario: Be able to evaluate text along with a positive-negative scale
Which of the following would you map to this requirement?
A. Key phrase extraction
B. Translation
C. Sentiment analysis
D. Language modeling
Answer
C. Sentiment analysis
Explanation
Sentiment analysis is a natural language processing (NLP) technique that evaluates text to determine the emotional tone or attitude, classifying it on a scale from positive to negative. It is the most appropriate choice for a scenario that requires assessing text sentiment along a positive-negative scale.
The other options are used for different NLP tasks:
A. Key phrase extraction identifies important words and phrases but does not evaluate sentiment.
B. Translation converts text from one language to another.
D. Language modeling predicts the next likely word in a sequence based on the context of previous words.
Therefore, sentiment analysis (C) is the right type of NLP to map to the requirement of evaluating text on a positive-negative scale. It will determine whether the text expresses a favorable, unfavorable, or neutral opinion and is the best fit for this sentiment classification scenario.
For evaluating text along a positive-negative scale, Sentiment Analysis is the appropriate natural language processing workload. Sentiment analysis determines the sentiment expressed in a text, categorizing it as positive, negative, or neutral. So the correct answer is Sentiment analysis.
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