Topic modeling on health journals with regularized variational inference

Robert Giaquinto, Arindam Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 (RVI) 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.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI Press
Pages3021-3028
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - 2018
Externally publishedYes
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

ASJC Scopus subject areas

  • Artificial Intelligence

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