Answer: Supervised Learning

## Explanation This is a classic **supervised learning** scenario because: 1. **Labeled Data**: The company already has 10,000 reviews with known sentiment labels (positive, negative, or neutral). 2. **Classification Task**: The goal is to predict categorical labels (sentiment categories) for new, unseen reviews. 3. **Training Process**: The algorithm can be trained on this labeled dataset to learn patterns that distinguish between positive, negative, and neutral reviews. **Why other options are not appropriate:** - **A) Unsupervised Learning**: Used when there are no labels available. The algorithm tries to find patterns or groupings in the data without guidance. - **C) Reinforcement Learning**: Used for sequential decision-making problems where an agent learns through trial and error with rewards/penalties. - **D) Self-supervised Learning**: A subset of unsupervised learning where the model generates its own labels from the data structure (e.g., predicting missing words in a sentence). Since the company already has labeled data, supervised learning is the most appropriate approach for this sentiment classification task.

Author: Jin H