Graph Analytics and Sentiment Analysis with ArangoDB

Aryan Jindal
4 min readApr 25


This blog is about our project on Graph Analytics and Sentiment Analysis with ArangoDB and Neo4j

Sentiment analysis is a powerful tool used to extract meaning and insight from written text. By analyzing the tone and emotion behind words, businesses and organizations can better understand their customers, improve their products and services, and even identify potential risks or opportunities.

One powerful technology for sentiment analysis is Neo4J, a graph database that enables fast, scalable, and flexible analysis of complex data. In this blog, we’ll explore how Neo4J can be used for sentiment analysis and some of the benefits it provides.

What is Neo4J?

Before diving into sentiment analysis, it’s important to understand what Neo4J is and how it works. Neo4J is a graph database, which means it stores data as nodes and relationships between those nodes. This structure allows for complex data to be represented in a way that is easy to query and analyze.

Neo4J is particularly useful for handling large and complex datasets, such as those generated by social media platforms. By using graph algorithms and queries, users can extract meaning and insights from data that would be difficult or impossible to do with traditional databases.

What is Sentiment Analysis?

Sentiment analysis is the process of analyzing written text to determine the emotional tone behind it. This analysis can help businesses and organizations understand how their customers feel about their products or services, identify potential risks or opportunities, and even predict future behavior.

There are several approaches to sentiment analysis, including rule-based analysis and machine learning. In rule-based analysis, experts create a set of rules that the analysis follows to determine the sentiment of a given text. In machine learning, algorithms are trained on large datasets to automatically identify patterns and make predictions.

How Can Neo4J be Used for Sentiment Analysis?

Neo4J’s graph database structure makes it ideal for sentiment analysis. By representing text data as nodes and relationships, users can easily analyze the sentiment of individual words, phrases, and entire texts.

One approach to using Neo4J for sentiment analysis is to create a graph of all the words in a given text and their relationships to each other. By assigning each word a sentiment score, users can then calculate the overall sentiment of the text based on the sentiment of its individual words.

Another approach is to use Neo4J to analyze social media data. By creating a graph of all the users on a social media platform and their relationships to each other, businesses and organizations can analyze the sentiment of their customers and identify trends and patterns in their behavior.

Benefits of Using Neo4J for Sentiment Analysis

Using Neo4J for sentiment analysis provides several benefits over traditional databases and analysis methods. These benefits include:

1. Scalability: Neo4J’s graph database structure allows for the efficient analysis of large and complex datasets.

2. Flexibility: The ability to represent data as nodes and relationships allows users to analyze data in a way that is tailored to their specific needs.

3. Speed: Neo4J’s optimized graph algorithms and queries enable fast analysis of even the largest datasets.

4. Accuracy: By using machine learning algorithms and sentiment analysis libraries, users can achieve high levels of accuracy in their sentiment analysis.


In our project, we’ll be trying to learn how we can use sentimental analysis on Twitter data for hashtags and whatnot and visualise it using the Neo4j graph database. Our man goal was to show how it can be used to create a visual representation of the relationship of Twitter users and their feelings. In this way, we will be able to gain insights in different areas where this analysis can be applied; For example, Marketing, Politics, and Social Media monitoring. We start by collecting a dataset of the Twitter data related to a specific topic or even using its API. We then run sentimental analysis on these tweets using an ML algorithm that will categorise each tweet as positive, negative, or neutral based on the emotion expressed from the text.

Once it seems like we have what we need, we store them into the Neo4j graph database. The nodes in the data will represent Twitter users, and the edges will their emotions.

After this we used the given database to create and visualise a graph that shows the relationship of the users and their sentiments.


In this project, we used Neo4j to plot graphs, we explored many ways on how to do so and were able to successfully complete our work. We started off by collecting Twitter data by its API and then loaded it into a dataset. Neo4j was able to successfully create graphs based on the given data. During our study we were able to find the benefits of Neo4j for sentimental analysis and its visualisation, which included its ability to process large data, provide a flexible data model and enable efficient graph traversal.

We also realised that Ne04j, via a grasp of emotion, offers really useful insights on social media trends. Finally, we were able to demonstrate a variety of applications for this analysis in various fields, such as predicting election results, keeping an eye on social media, and determining consumer preferences.
We’re pleased to have shown the value of a graph database for emotional analysis and its display at this point. In the future, this might be a useful tool for social media analysis scholars, practitioners, and policymakers.


Sentiment analysis is a powerful tool for understanding the emotions and attitudes behind written text. By using Neo4J’s graph database structure, businesses and organizations can efficiently analyze large and complex datasets to extract meaningful insights and improve their products and services.

Whether analyzing social media data or individual texts, Neo4J provides a flexible, scalable, and fast solution for sentiment analysis that can help businesses and organizations better understand their customers and make informed decisions.