Sentiment Analysis Twitter Microservices Example

A sample application that demonstrates how to build a graph processing platform to analyze sources of emotional influence on Twitter. A guided tutorial is provided with this sample project.

This repository is actively being worked on and has not yet transformed into its final state. Please check back periodically to see updates. If you have trouble running the example, please post to the issue tracker.

Architecture

The diagram below shows each component and microservice as a part of this sample application. The connections are communication points between each service, describing what protocol is used.

The two Spring Boot applications that are colored in blue are stateless services. Stateless services will not attach a persistent backing service or need to worry about managing state locally.

The Spring Boot application that is colored in green is the Twitter Crawler service. Components that are colored in green will typically have an attached backing service. These backing services are responsible for managing state locally, and will either persist state to disk or in-memory.

The services colored in red are external APIs that are used to collect data and to run sentiment analysis and other natural language machine learning algorithms.

Spring Boot Services

Rank Dashboard
Discovery Service
Configuration Service
Twitter Crawler

Backing Services

Neo4j (BOLT)
RabbitMQ (AMQP)
Twitter API (HTTP)
Google Cloud Language API (HTTP)

Graph Processing Platform

Neo4j is a graph database that includes graph processing algorithms from a community plugin called APOC.

Graph Data Model

The graph data model in Neo4j will be created using the following diagram.