Typically, LDA iterates over the corpus until a convergence requirement is met, but in this case this is neither practical (due to the size of our corpus) nor necessarily desirable; the diminishing effects of the initial seeding means that the model may not stabilize, in terms of its likelihood, until after it has shifted away from our desired stylistic dimensions towards some other variation in the data.
we treat the optimal number of iterations as a variable to investigate.