Answer: Supervised Learning

## Explanation This is a **Supervised Learning** scenario because: - **Labeled Data**: The company has 10,000 reviews with known sentiment labels (positive, negative, or neutral) - **Classification Task**: The goal is to classify new reviews into predefined categories - **Training Process**: The algorithm can learn from the labeled examples to predict sentiment for new, unlabeled reviews ### Why Other Options Are Incorrect: - **A) Unsupervised Learning**: Used when there are no labels and the algorithm must find patterns or groupings on its own - **C) Reinforcement Learning**: Used for 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 ### Key AWS Service for This Use Case: - **Amazon Comprehend**: AWS's natural language processing service that can perform sentiment analysis - **Amazon SageMaker**: For building custom machine learning models for text classification

Author: Ritesh Yadav