  Topic modeling enables exploration and compact representation of a corpus.
The CaringBridge (CB) dataset is a massive collection of journals written by
patients and caregivers during a health crisis. Topic modeling on the CB
dataset, however, is challenging due to the asynchronous nature of multiple
authors writing about their health journeys. To overcome this challenge we
introduce the Dynamic Author-Persona topic model (DAP), a probabilistic
graphical model designed for temporal corpora with multiple authors. The
novelty of the DAP model lies in its representation of authors by a persona ---
where personas capture the propensity to write about certain topics over time.
Further, we present a regularized variational inference algorithm, which we use
to encourage the DAP model's personas to be distinct. Our results show
significant improvements over competing topic models --- particularly after
regularization, and highlight the DAP model's unique ability to capture common
journeys shared by different authors.
