This is the third (first, second) analysis review in this series, and it is the third tool produced by Voyant.
RezoViz takes a corpus of text, identifies which words appear in documents together, and assigns a weight according to the frequency of that appearance. For example, one document that has the words Istanbul, Erdogan, and Minister and a document with Istanbul, Erdogan, and CHP will add 1 unit of weight to the link between Istanbul and Erdogan. Across many documents, you can begin to see which topics are discussed together. Below is an example of a graph that can be drawn given this data.

The green lines represent the words between which there are links, i.e. between the words that appear in the same document. The numbers in circles represent the ‘weight’ with which each have a link, i.e. how many documents they appear in together. In this image, the terms displayed are the ones with the most links (above whatever threshold). I’ve highlighted the term ‘Turkey’, so that linked terms with ‘Turkey’ appear in red.
Several types of questions can be derived from this kind of representation. Are there unexpected links between terms? Are there no links between terms that would be expected to have links? What can the weights of each link tell us about the importance of links?
Continue reading “Asking new questions, part 3 – RezoViz” →
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