Data & Analytics

Discovering Hot Topics Using Machine Learning


The Discovering Hot Topics Using Machine Learning solution is designed to help businesses identify dominant topics related to their products, events, brands, and policies. By analyzing data from sources like Twitter, RSS news feeds, YouTube comments, and Reddit, this solution enables businesses to react swiftly to new growth opportunities, address negative brand associations, and enhance customer satisfaction. The solution leverages machine learning techniques for topic modeling, sentiment analysis, and contextual understanding.


  1. Secure One-Click Deployment:
    The solution provides a secure and easy deployment process through an AWS CloudFormation template aligned with AWS Well-Architected Framework methodologies.
  2. Near Real-Time Analytics:
    Data streams containing text and images are ingested and analyzed in near real-time. Topic modeling is applied to detect dominant topics, and the solution identifies the key terms forming a topic within customer feedback.
  3. Multi-Lingual Data Ingestion:
    Amazon Translate is used to ingest data in multiple languages. The solution identifies customer sentiment and employs contextual semantic search to comprehend the nature of online discussions.
  4. Pre-Built QuickSight Dashboard:
    The solution includes a pre-built Amazon QuickSight dashboard that allows users to visualize large-scale customer analyses. This enables insights in near real-time, aiding in understanding context, threats, and opportunities quickly.

Technical Details:

The solution’s technical implementation involves several components built using the AWS Well-Architected Framework and its pillars. The architecture includes:

  • Ingestion (Step 1): Lambda functions, Amazon DynamoDB, and Amazon EventBridge manage social media and RSS feed ingestion. For detailed architecture diagrams, refer to the implementation guide.
  • Data Stream (Step 2): Data is buffered through Amazon Kinesis Data Streams to ensure resiliency and throttle incoming requests. Data Streams have a configured Dead-Letter Queue (DLQ) to capture processing errors.
  • Workflow (Step 3): The consumer (Lambda function) of Kinesis Data Streams initiates a Step Functions workflow that orchestrates Amazon Machine Learning capabilities including Amazon Translate, Amazon Comprehend, and Amazon Rekognition.
  • Integration (Step 4): Inference data integrates with storage components using an event-driven architecture facilitated by Amazon EventBridge. Further customization is possible by configuring rules.
  • Storage and Visualization (Step 5): Components like Kinesis Data Firehose, Amazon S3 buckets, AWS Glue tables, Amazon Athena, and Amazon QuickSight provide storage and visualization capabilities.

The architecture ensures adherence to the AWS Well-Architected Framework’s operational excellence, security, reliability, performance efficiency, and cost optimization pillars, resulting in a secure, high-performing, resilient, and efficient infrastructure.

In summary, the Discovering Hot Topics Using Machine Learning solution provides businesses with actionable insights by analyzing data from various sources and identifying dominant topics. With near real-time analytics and pre-built visualization dashboards, this solution enables businesses to make informed decisions and respond effectively to customer feedback and trends.

Discovering Hot Topics Using Machine Learning architecture flow diagram
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