Dirichlet distribution is widely used in document modelling and document clustering.
I tried to understand its rational. I read from this article that:
Different Dirichlet distributions can be used to model documents by different authors or documents on different topics.
So how could we tell whether it is modelling about different authors or about different topics? This is important because in a document clustering task, it directly dictates the semantic of the clustering result.
And I found it too subjective to limit the possible aspects of modelling to only author or topic. Since there seems to be no strong evidence to favor a specific aspect, it could be any other potential/latent aspect.
Could anyone shed some light on this?