Learn how AI systems use sentiment analysis to classify movie reviews as positive or negative. Expert-level explanation for the IBM AI Fundamentals certification exam.
Table of Contents
Question
You are reading reviews of the latest blockbuster movie. One review says, “This movie is fantastic!” Another review says, “Worst movie ever!”
Which of the following best describes the process that an AI system would use to classify the reviews?
A. Keyword analysis
B. Emotion detection
C. Sentiment analysis
D. Topic modeling
Answer
C. Sentiment analysis
Explanation
Sentiment analysis is a means of assessing if data is positive, negative, or neutral.
Sentiment analysis is a natural language processing (NLP) technique used to determine the emotional tone or opinion expressed in a piece of text. It goes beyond simple keyword analysis by considering the context and language used to express sentiment.
In this case, sentiment analysis would look at phrases like “fantastic” and “worst movie ever” to classify one review as very positive and the other as very negative, respectively. The AI system is trained on large datasets of text labeled with sentiment to learn the linguistic patterns and word associations that indicate positive or negative opinions.
While keyword analysis (A) could help identify the subject matter, it wouldn’t capture the opinions expressed. Emotion detection (B) is a related but distinct task focused on identifying specific emotions like joy, anger, or sadness. Topic modeling (D) aims to discover the key topics or themes across a collection of documents, rather than classifying the sentiment of individual reviews.
Therefore, sentiment analysis (C) is the most appropriate technique for classifying the polarity of opinions expressed in these contrasting movie reviews.
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