I created a topic model which outputted 11 topics out of 437 terms on ~60000 small documents.
I wanted to show how good each topic is. But I don't know what "good" means in this case.
Here's the distribution of the relative scores of each term for its topic. (terms are in the x-axis, ordered by relevance for the topic).
It's possible to notice that some topics are well represented by a short number of highly associated terms like V1, V7, V9 and V11, while other topics are not very well clarified, like V2 and V3.
Which numerical summary statistics could help me quantify these characteristics (or other I didn't think of)?