Text Mining and Topic Modeling
with Text Network Analysis

InfraNodus represents any text as a network to reveal the main topics, sentiment, most influential keywords, and interesting relations in any discourse.



 
Watch an Introduction
 

 

Text mining and topic modeling using network analysis

Text Mining Features

Text network analysis can be used to obtain insights about a text's structure, reveal the most important topics, and the connections between them.

1. Live Text Input
and Data Import

Write or import a text, PDF, CSV spreadsheets, Tweets, RSS news feeds, Google search results...

2. Topic Modeling
and Text Classification

Using network analysis tools we will identify and visualize the main nodes, keywords, topical clusters, and relations between them.

3. Network Analysis
and Visualization

The data is represented as a network graph, which reveals patterns, relations, and can be used for presenting your insights.



Text Analytics

Word frequency, historical trends, topical groups, most influential ideas, structural gaps in a discourse, and more.

Feature Extraction

Tag your text data and export our insights to integrate into your own machine-learning model.

Sentiment Analysis

Reveal the sentiment in your data using AFINN or ML-based BERT AI model: positive, negative, neutral.

Voice of the Customer

Product review analysis: the main topics, contextual relations, sentiment. Upload or import from Amazon / Google.

Multilingual

Use English, German, French, Russian, Spanish, Portugese, Swedish, Chinese, etc.

Share & Export Data

You can export the insights as a spreadsheet for further analysis or as hi-res PNG or SVG images.

Private by Default

All your data is private by default and you can export and erase it anytime.

AI-powered Insights

You can use powerful AI algorithms to find the gaps in a discourse or categorize your data.




InfraNodus Workflow

 

You can use standard text, hashtags or file import to add your data.

Text to network graph visualization

Reveal the patterns in your data using network analysis.

Topic modeling and text mining using text network analysis

Use the interactive graph to find the most relevant excerpts.

Get the relevant excerpts using the graph

Find the structural gaps so you know where the new ideas are hiding.

Structural holes in knowledge graphs generate new ideas


Network Topic Modeling Workflow


Selected Use Cases

InfraNodus can be used for text mining, sentiment analysis, social and discourse network analysis, and creative writing.


 

Use Case: Text Mining and Topic Modeling



Problem: What are the main topics inside a text?

The current solutions are either too simplistic or too technically challenging.


 
Solution: Topic modeling based on text network analysis and visualization.

InfraNodus will represent the text as a network and use powerful graph analysis algorithms to identify and visualize the main keywords, topics, and their relations.


Learn More    Try It Out

Use Case: Discourse Structure and Bias Analysis



Problem: How to identify the level of diversity and bias in a text or a network?

Suppose that we want to study the current discourse on coronavirus. What are the main topics and how diverse is the media coverage of this subject? What is the level of urgency?


 
Solution: Represent the text as a network and measure the diversity and bias of the graph structure.

InfraNodus represents any text as a network, so you can get insights about the structure of the discourse.

Based on the graph's modularity and distribution of influence across the different communities, we can measure the diversity of the discourse stucture and estimate its bias.

Watch the Video    Try It Out



 

Use Case: Creative Writing & Idea Generation



Problem: How to generate new ideas for your writing or at a brainstorming session?

Whether you are brainstorming, doing research or writing you may experience writer's block or simply get stuck for new ideas.


 
Solution: InfraNodus will represent your text as a network providing a visual overview and helping you find the gaps where the new ideas are hiding.

Our studies have shown that network representation of text encourages one to think in terms of connections, helping you make the new links between the different parts of your text and make your discourse more coherent.

You will also be able to see the structural gaps in your writing: that's where the new ideas our hiding.

Learn More    Try It Out

Use Case: Sentiment Analysis of Open Survey Customers Answers



Problem: Need to know what your customers think?

Open survey answers are hard to analyze. Positive / negative is not enough and tag clouds lose out the context.


 
Solution: Find the patterns in their open survey answers using network graphs.

With InfraNodus your customers' feedback will be visualized as a word graph where the words used in the same answer are connected.

This representation will reveal the patterns and context in your customers' answers, so you will see the main topics that appear in their feedback as well as the hidden relations between them that can't be uncovered by other tools.

Read the Case Study    Try It Out

 



 


See a Demo Graph     Create Your Own

 



 


How Text Network Analysis Works

Network analysis and graph visualization help you cope with the informational overload and make sense of data.

Here is how it works:

1

1. Add Any Text or Data

You can use this approach for your research, creative writing, marketing feedback study, SEO purposes, news analysis, etc.

Add any text (speech-to-text available), notes, or import your Gephi graph, Evernote data, Twitter feed, or Google search results snippets.

The text is converted into a graph where the words are the nodes and the co-occurrences are the connections between them, so you can see all your disjointed bits and pieces of data at once, as a big picture.

More in our Whitepaper
Create a Text Network Graph
2

2. Get a Visual Overview

The network will then be generated from the text or data you added. The most influential words in the graph (nodes with the highest betweenness centrality) are shown bigger, while the words (nodes) that occur more often together are aligned into topical clusters and have distinct colors.

The graph will show you not only the main topics and the most influential terms, but also the relations between them, making it a much better visual summary tool than the traditional tagclouds.

Overview Data with Text Network Visualization
3

3. Generate Insight

InfraNodus will identify the structural gaps in the network: parts of the graph that could be connected but are not.

It will propose you the different ways how you can bridge these gaps to produce insight and generate new ideas.

You can also export your data as a CSV file (with the InfraNodus insights) for further analysis or to use as a training data for your neural network applications.

Generating Insight with Structural Gaps



Our Methodology in Detail

To learn more about the algorithms used in InfraNodus and for citations, please, refer to this peer-reviewed paper:

Paranyushkin, D (2019). InfraNodus: Generating Insight Using Text Network Analysis, Proceedings of WWW'19 The Web Conference, www.infranodus.com (ACM library, PDF).

If you want to know more about the algorithms used, here is the first paper we published on the subject:

Paranyushkin, D (2011). Identifying the pathways for meaning circulation using text network analysis, Nodus Labs. (Google scholar)


Become a Supporter!

InfraNodus is open-source software and is developed without external funds or investors. We develop and maintain this tool and the associated research based on your contributions only. We believe it is a working model for crowdfunded software made by users for users.

You can support our work if you become a subscriber and get access to the cloud version or if you become our patron on Patreon. Enterpise, government, and educational versions are also available.

Pricing Options

This tool and the associated research in ecological network thinking are developed solely on the monthly contribution provided by users like you. We believe this is a working model for crowd-funded R&D, and we thank you for your support!

All quotas are per each text. There is no limitation on the number of texts you can process every month.

About InfraNodus

Gregory Bateson coined a beautiful term: "ecology of the mind". What is a mind that is ecological? It has the ability to have an overview, but it can also zoom into any idea. It embraces diversity, but it can also obsess over one thing when needed. It can discover the obvious, but it can also reveal the things that are hidden and ponder the gaps that have not yet been bridged. Rational and poetic.

InfraNodus is a tool that is developed to help you think this way. It is made to promote ecological dynamics and diversity on the cognitive level. InfraNodus visualises and analyses ideas as a network, revealing the relations and patterns within them, so you can understand the dynamic complexity of how knowledge evolves and explore the nuances of meaning.

We believe that it is especially important today to make this kind of instruments available to everybody. That's why we develop InfraNodus so that it can accessible to everyone, solely thanks to our users' monthly contributions and based on their feedback. This is not a start-up, we don't have investors and an exit strategy. We are here to stay and to evolve.

We are constantly improving InfraNodus based on our users' feedback. We are also currently developing an API to make this way of thinking available to other applications and to enable the use of InfraNodus insights for machine learning and AI applications.

If you like our cause, please, sign up for an account and give it a try, support us on Patreon, or simply let us know that it resonates, because this is the best way to motivate each other.

Dmitry Paranyushkin, the founder


 

 

Testimonials

InfraNodus is used by researchers, writers, marketing professionals and corporations. Here's what some of our supporters have to say:

Technical Support

Check out our support pages, so you can see how to create the new network graphs, how to read and interpret them, how to discover the hidden patterns in your data and share the results of your research.

1. How to Create a New Graph

The most basic way to create a new graph is to use the hashtags. Each hashtag is a node, their co-occurrence is the connection between them. You can also use normal text to build your text graphs automatically. More on Creating the Graphs

2. How to Read and Interpret Network Visualization

Studies have shown that diagrams and especially network representation of data makes you think more of connections in your data. You can identify the main elements as well as the relations between them and — most importantly — discover the gaps in your knowledge using the graphs. More on Reading the Graphs

3. Find the Right Excerpt in Text

Network graphs are great for non-linear reading. Look at the graph, find the part you like, click on the nodes, and you'll see the excerpt of your original data that contains this exact combination. More on Nonlinear Reading

4. How to Import Text and PDF files

You can import and visualize your text files or PDF documents. In order to do that, just upload the file and InfraNodus will do the rest. More on Visualizing PDFs