A computational linguist uses a large text corpus to train a sentiment analysis model; if the corpus contains proportionally fewer examples of nuanced negative expressions, which consequence follows?

💡 Explanation

The model exhibits decreased precision because sentiment analysis relies on the frequency of specific words and phrases in the training data; therefore, fewer nuanced negative examples cause the model to misclassify subtleties, rather than properly discern accurate negative expressions.

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