Text visualization can be used to discern patterns from data and gain new insights into any discourse. Using the graph on this page you can explore a selection of the text visualization software available today and see how the different tools are related to each other.

There is a wide range of approaches used in text visualization starting from the simple tag clouds to the more complex tools based on natural language processing and various statistical techniques.

Tag cloud generators are often used for illustrative and marketing purposes. The most frequent words are shown bigger on the map, so the word cloud shows the most relevant terms in the text. While those word maps are easy to use they lack contextual information, which increases the risk of an error and makes it impossible to decipher the complexity of meanings present within a text. Collocate clouds attempt to solve this problem, but they lack dimensionality.

Other approaches use various statistical methods and visualizations in order to help the researchers detect some interesting patterns and relations in their data. Topic mining, sentiment analysis, entity recognition and other approaches can be used together to provide an interesting perspective on text corpora.

Text network analysis is interesting because it combines both statistical tools and easy-to-read visualization. A text is represented as a network, so that the most influential words can be seen on the graph as well as the connections between the different terms used in the text. This allows the researchers to see the various contexts present within the text and how they are related. It also can be used to detect the structural gaps in a discourse in order to discover the parts of the discourse where there’s a potential for new ideas.

This visualization is made using open-source text network analysis tool InfraNodus, which you can try out online on any text, research notes, Google search results, Tweets, etc.