Architecture Overview - AI-Driven Social Media Dashboard

Architecture Overview

Deploying this solution builds the following environment in the AWS Cloud.


        AI-Driven Social Media Dashboard solution - architectural overview

Figure 1: AI-Driven Social Media Dashboard architecture on AWS

The AWS CloudFormation template deploys an Amazon Elastic Compute Cloud (Amazon EC2) instance 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 in the Amazon S3 bucket.

The solution 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 (ai_driven_social_media_dashboard) which is used to organize the tables for the data in Amazon S3. Athena uses these table definitions to query the data stored in Amazon S3 and return the information to an Amazon QuickSight dashboard.


        Sample Amazon QuickSight dashboard for visualizing tweet analysis

Figure 2: Sample Amazon QuickSight dashboard for visualizing tweet analysis