A framework for streamlined statistical prediction using topic models
Table 2: Table of the percentage of hard classifications of storylines for each left-out scene in the corpus that are correct, alongside the Brier score, for each model.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] Brier score']
One of the motivating ideas behind having topic dependencies between consecutive words, as in the HMTM model, is that some documents will have a predisposition to stay in the same topic for a long sequence, such as a sentence or a paragraph. This argument particularly applies to narrative-driven corpora such as the Love Actually corpus. To that end, we may adapt the HMTM described above so that the model favours long sequences of the same topic, by adjusting the Dirichlet priors of the transition probabilities, γ={γ1,... ,γk}, to favour on-diagonal elements. By specifying these priors to be γls={0.99+0.01/kifl=s0.01/kelsewhere, for l=1,2,... ,k, we choose the persistent HMTM regression model with three topics. This results in a Brier score of 0.9124, which is a massive improvement on the original HMTM regression model and makes it very competitive with the word count model.