Sentiment Analysis on Twitter Data Using Neo4j and Google Cloud

In this blog post, we’re going to walk through designing a graph processing algorithm on top of Neo4j that discovers the influence and sentiment of tweets in your Twitter network.

The source code for this reference application is open source. You can find the GitHub project here.

Graph Data Modeling

The first thing we’ll need to do is to design a data model for analyzing the sentiments and influences of users on Twitter. This example iterates from an earlier graph processing example described in another blog post. I recommend taking a look at that post to better understand the concepts I talk about in this one.

The diagram below is the graph data model that we will use to import, analyze, and query data from Twitter.

In the diagram above, the following relationships are described.

• Users follow other users

• Users create tweets

• Tweets contain phrases

• Phrases are categorized into topics

Twitter User Ranking

For this first blog post we’re going to focus on generating a rank of influential Twitter users in my social network that tells me which topics a user tweets about.

The screenshot above is from the results of a Neo4j cypher query. Here we find a list of Twitter users that were discovered using a crawling algorithm based on PageRank. This output is similar to the dashboard that was created in an earlier blog post, but adds in a top category, top phrase, and a sentiment score.

Deep Learning Sentiment Analysis for Movie Reviews using Neo4j

While the title of this article references Deep Learning, it's important to note that the process described below is more of a deep learning metaphor into a graph-based machine learning algorithm. No neural networks are used.

Sentiment analysis uses natural language processing to extract features of a text that relate to subjective information found in source materials.

Movie Review Sentiment Analysis

A movie review website allows users to submit reviews describing what they either liked or disliked about a particular movie. Being able to mine these reviews and generate valuable meta data that describes its content provides an opportunity to understand the general sentiment around that movie in a democratized way. That’s a pretty cool thing if you think about it. Using machine learning we can democratize subjectivity about anything in the world. We can make an objective analysis of subjective content, giving us the ability to better understand trends around products and services that we can use to make better decisions as consumers.