AI-Driven Social Media Dashboard helps you to gain insights into your customer’s conversations and deepen brand awareness by analyzing social media interactions. It automatically provisions and configures the AWS services necessary to capture multi-language tweets in near real-time, translate them, and store both the raw and enriched datasets durably in the solution's data lake. You can then analyze this data and create meaningful dashboards powered by Amazon QuickSight to visualize and understand customer sentiment.
Overview
AI-Driven Social Media Dashboard monitors and ingests specified tweets using stream processing and leverages a serverless architecture and ML services (Amazon Translate and Amazon Comprehend) to translate and extract insights from those tweets. The diagram below presents the architecture you can build using the example code on GitHub.
AI-Driven Social Media Dashboard architecture
AI-Driven Social Media Dashboard deploys an Amazon Elastic Compute Cloud (Amazon EC2) instance running in an Amazon Virtual Private Cloud (Amazon VPC) that ingests tweets from Twitter. An Amazon Kinesis Data Firehose delivery stream loads the streaming tweets into the raw prefix in the solution's Amazon Simple Storage Service (Amazon S3) bucket. Amazon S3 invokes an AWS Lambda function to analyze the raw tweets using Amazon Translate to translate non-English tweets into English, and Amazon Comprehend to use natural-language-processing (NLP) to perform entity extraction and sentiment analysis.
A second Kinesis Data Firehose delivery stream loads the translated tweets and sentiment values into the sentiment prefix in the Amazon S3 bucket. A third delivery stream loads entities in the entities prefix using in the Amazon S3 bucket.
The Guidance also deploys a data lake that includes AWS Glue for data transformation, Amazon Athena for data analysis, and Amazon QuickSight for data visualization. AWS Glue Data Catalog contains a logical database which is used to organize the tables for the data on Amazon S3. Athena uses these table definitions to query the data stored on Amazon S3 and return the information to an Amazon QuickSight dashboard.
Additional resources
Features
Machine Learning
Visualization
Automation
Browse our library of AWS Solutions to get answers to common architectural problems.
Find prescriptive architectural diagrams, sample code, and technical content for common use cases.