Generate a Word Cloud with a Context


Most word cloud visualization tools lack the context. They will only show the main keywords but omit the relations between them. As a result, it is very easy to miss out on important insights and take wrong decisions.

InfraNodus solves this problem. Using advanced text network analysis algorithms, it studies the structure of the original text and shows not only the most important terms, but also how they are related. The words that tend to appear more often next to each other are shown closer in the word cloud, so it is easy to discover which concepts belong together in topics and make a well-informed decision based on this extra piece of contextual information.

Word cloud visualization with a context, made using InfraNodus

There are multiple settings and insights that allow you to find out more information the text, if you need to. For example, you can turn on the display of relations between the words and add some analytical information to the graph, to show which topical clusters are the most releveant to a particular text:

Text analytics for the word clouds

Additionally, all InfraNodus word clouds are fully interactive and customizeable. You can remove the words that may seem not relevant, merge the words into phrases, select the words to see in which context they're used, change the colors, and drag the words around the graph to position them better. This way, you can use InfraNodus as a visual editor to help yourself deliver the most compelling visual story that will support your narrative.

An example of the workflow that can be used with InfraNodus word clouds

Simply copy and paste your text or import a PDF, text, CSV, or MD file into InfraNodus, and you will have a quick and precise overview of the main concepts and their relations within.



Try It Yourself


To generate a word cloud with InfraNodus, please, create an account:


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How do Contextual Word Clouds Work?


The basic approach is based on text network analysis and is described in our peer-reviewed paper paper: InfraNodus: Generating Insight Using Network Analysis (Paranyushkin, 2019) (read it for free in the ACM library).

Every word in a text is represented as a node in a graph and the words' co-occurrences are represented as the edges.

Next, we apply several node ranking and community detection measures used in network science, which range the nodes by betweenness centrality (discoursive influence) and groups them by communities (the nodes that are more densely connected together belong to the same topic, have the same color, and are located closer on the graph.

This approach is not usually used in generating word clouds, but it provides additional and important information about the structure of the text and the context. We then remove the nodes and the edges, leaving only the words, providing a traditional word cloud view. If you prefer, you can turn on the display of nodes and edges, for a more precise and "tech" look.

The insights obtained using network analysis are fully available in the word cloud view as well. You can see what are the main topics within the text, how they are related, and — more importantly — what are the structural gaps in the discourse where you could possibly geneate new ideas.



Custom Interactive Mind Mapping using Network Visualization


Custom interactive mind maps can be very useful for representing knowledge within any organization.

Contact us if you are interested in a particular use case or if you have any questions about creating a mind map based on your own source data.


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