Answer: Supervised Learning

## 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.

Author: Ritesh Yadav