Identify the most dominant topics associated with your products, brands, and topics relevant to your business

Publication date: August 2020 (last update: August 2024)

The Discovering Hot Topics Using Machine Learning solution identifies the most dominant topics associated with your products, policies, events, and brands. This enables you to react quickly to new growth opportunities, address negative brand associations, and deliver a higher level of customer satisfaction for your business. In addition to helping you understand what your customers are saying about your brand, this solution gives you insights into topics that are relevant to your business.

This solution deploys an AWS CloudFormation template to automate data ingestion from these sources:

• RSS news feeds

• YouTube comments tied to videos

• Reddit (comments from subreddits of interest)

• Custom data in JSON or XLSX format

This solution uses pre-trained machine learning (ML) models from Amazon Comprehend, Amazon Translate, and Amazon Rekognition to provide these benefits:

• Detecting dominant topics using topic modeling—identifies the terms that collectively form a topic.

• Identifying the sentiment of what customers are saying—uses contextual semantic search to understand the nature of online discussions.

• Determining if images associated with your brand contain unsafe content—detects unsafe and negative imagery in content.

• Helping you identify insights in near real-time—uses a visual dashboard to understand context, threats, and opportunities almost instantly.

The solution can be customized to aggregate other social media platforms and internal enterprise systems. The default CloudFormation deployment sets up custom ingestion configuration with parameters and an Amazon Simple Storage Service (Amazon S3) bucket to allow Amazon Transcribe Call Analytics output to be processed for natural language processing (NLP) analysis.

With minimal configuration changes in the custom ingestion functionality, this solution can ingest data from both internal systems and external data sources, such as transcriptions from call center calls, product reviews, movie reviews, and community chat forums including Twitch and Discord. This is done by exporting the custom data in JSON or XLSX format from the respective platforms and then uploading it to an Amazon Simple Storage Service (Amazon S3) bucket that is created when deploying this solution. For more details on how to customize this feature, see Customizing Amazon S3 ingestion.

After you deploy the solution, you can use the included Amazon QuickSight dashboard to visualize the solution's ML inferences.

This implementation guide describes architectural considerations and configuration steps for deploying this solution in the Amazon Web Services (AWS) Cloud. This solution's AWS CloudFormation template launches and configures the AWS services required to deploy the solution using AWS best practices for security, availability, performance efficiency, and cost optimization.

This solution is intended for deployment by IT Specialists, IT Architects, Administrators and DevOps professionals with experience in the AWS Cloud.

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Know the cost for running this solution. The estimated cost for running this solution in the US East (N. Virginia) Region is USD $375.00 per week for AWS resources.	Cost
Understand the security considerations for this solution.	Security
Know how to plan for quotas for this solution.	Quotas
Know which AWS Regions support this solution.	Supported AWS Regions
View or download the AWS CloudFormation template included in this solution to automatically deploy the infrastructure resources (the "stack") for this solution.	AWS CloudFormation template
Access the source code and optionally use the AWS Cloud Development Kit (AWS CDK) to deploy the solution.	GitHub repository