In a modeling project, people evaluate phrases and provide reactions as the target variable for the model. Which of the following best describes what this model is doing?
→ Sentiment analysis refers to using machine learning or NLP techniques to determine the sentiment or emotional tone behind a body of text (e.g., positive, neutral, or negative). When people provide reactions to phrases, the model is learning to associate language with subjective emotion or opinion.
Why the other options are incorrect:
B: NER identifies entities (e.g., locations, organizations) — not emotions.
C: TF-IDF is a feature engineering method, not a modeling goal.
D: POS tagging classifies words by their grammatical function — not sentiment.
Official References:
CompTIA DataX (DY0-001) Official Study Guide – Section 6.3:“Sentiment analysis models associate textual input with subjective labels, such as emotional response or polarity.”
Applied Text Analytics, Chapter 8:“When modeling user reactions to text, sentiment classification techniques are commonly employed.”
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