A company already collects social media posts into an S3 bucket and needs a quick solution to evaluate sentiment, visualize trends, and configure alerts with minimal infrastructure and data science effort. Which combination of AWS services best achieves this quickly?

The correct option leverages Amazon Comprehend, a fully managed natural language processing (NLP) service that provides sentiment analysis out-of-the-box, requiring no model training or data science effort. A Lambda function triggered by S3 uploads calls Comprehend, then writes the results to a CloudWatch custom metric for trend visualization and alerting via CloudWatch Alarms, and also to S3 for persistent storage. This is a quick, serverless, and low-effort solution. The first incorrect option involves training a custom model in SageMaker, which requires significant data science effort and time, contrary to the "quick solution" and "minimal data science effort" requirements. The second incorrect option suggests using the semantic segmentation algorithm, which is for image processing, not text sentiment analysis. The third incorrect option uses Comprehend but relies on a scheduled Lambda to query DynamoDB for trends and send SNS notifications, which is less efficient for trend visualization and alerting than CloudWatch metrics and alarms.