Word clouds provide a quick and visually appealing way to showcase textual content and they have been in use since the 90s. However, they lack precision, interactivity, and context. We developed a methodology and a word visualization tool that can take word clouds to the next level, making them much more useful not only for visualization, but also for research and educational purposes.
In this tutorial we present a method for topic modeling using text network analysis (TNA) and visualization. The approach we propose is based on identifying topical clusters in text based on co-occurrence of words. We will demonstrate how this approach can be used for topic modeling, how it compares to Latent Dirichlet Allocation (LDA), and how they can be used together to provide more relevant results.
Designing efficient content structures that guide readers through in ways that are efficient, maintaining diversity and narrative of the original.
In this article we demonstrate how you can visualize Google search results as a network of interrelated terms to identify the most relevant groups of keywords.
Mental disorders and various self-imposed psychological obstacles often arise because of the lack of choice. Gregory Bateson wrote about the double-bind origin of schizophrenic behavior where “crazy” reaction is simply a way of transcending a limiting situation, when the person sees no other choice. Various psychotherapeutic practices focus on expanding the range of possibilities for the patient, offering more choices than is currently available. Text network visualization can be a useful tool to aid this process. In this case study we are going to demonstrate how it works.
