Topic Modeling on Health Journals with Regularized Variational Inference
This addresses the problem of analyzing temporal health data for patients and caregivers, but it is incremental as it builds on existing topic modeling techniques.
The authors tackled topic modeling on the CaringBridge health journal dataset, which is challenging due to multiple authors writing asynchronously, by introducing the Dynamic Author-Persona (DAP) model with regularized variational inference, resulting in significant improvements over competing models and the ability to capture shared health journeys.
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.