Once we have our topic model of choice set up, we can use it to analyze our corpus, and also get some more insight into the nature of our topic models. While it is certainly useful to know what kind of topics are present in our dataset, to go one step further we should be able to, for example, cluster or classify our documents based on what topics they are made out of.
In our Jupyter notebook example from Chapter 8, Topic Models, let's start looking at document-topic proportions. What exactly are these? When we were looking at topics in the previous chapter, we were observing topic-word proportions - what are the odds of certain words appearing in certain topics. We previously mentioned that we assumed that documents are generated from topics - by identifying document-topic proportions, we can see exactly how the topics generated the documents.
So, do...
