GENERATIVE-AI-LEADER · Question #63
A company wants to build a model to classify customer reviews as positive, negative, or neutral. They have collected a dataset of thousands of customer reviews, and each review has been manually…
The correct answer is D. Supervised learning. Supervised learning (D) is the correct approach because the dataset has labeled examples - each review is manually tagged as positive, negative, or neutral. Supervised learning trains a model by learning the mapping between inputs (review text) and known output labels…
Question
A company wants to build a model to classify customer reviews as positive, negative, or neutral. They have collected a dataset of thousands of customer reviews, and each review has been manually tagged with the corresponding sentiment: positive, negative, or neutral. What machine learning should the company use?
Options
- ADeep learning
- BUnsupervised learning
- CReinforcement learning
- DSupervised learning
How the community answered
(27 responses)- A4% (1)
- B7% (2)
- D89% (24)
Explanation
Supervised learning (D) is the correct approach because the dataset has labeled examples - each review is manually tagged as positive, negative, or neutral. Supervised learning trains a model by learning the mapping between inputs (review text) and known output labels (sentiment), which is exactly this scenario. Unsupervised learning (B) is used when data has no labels and the model discovers patterns on its own (e.g., clustering). Reinforcement learning (C) involves an agent learning through trial-and-error rewards, not from labeled datasets. Deep learning (A) is a technique (a subset of ML using neural networks), not a learning paradigm - and while deep learning could be used here, supervised learning is the correct categorical answer.
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