Explanation

This is a supervised learning problem because:

1. Labeled Data Available: The company has 10,000 reviews with known sentiment labels (positive, negative, or neutral).
2. Classification Task: The goal is to classify new reviews into predefined categories.
3. Training with Labels: Supervised learning algorithms learn from labeled examples to make predictions on new, unseen data.

Why other options are incorrect:

• A) Unsupervised Learning: Used when there are no labels, and the algorithm tries to find patterns or groupings in the data.
• C) Reinforcement Learning: Involves an agent learning through trial and error by receiving rewards or penalties for actions in an environment.
• D) Self-supervised Learning: A subset of unsupervised learning where the data itself provides supervision, typically by creating pseudo-labels from the data structure.

Key Takeaway: When you have labeled data and want to predict categories or values for new data, supervised learning is the appropriate approach.