{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/topic-modeling-on-health-journals-with","title":"Topic Modeling on Health Journals with Regularized Variational Inference","arxiv_id":"1801.04958","date":"2018-01-15","proceeding":null,"authors":["Robert Giaquinto","Arindam Banerjee"],"abstract":"Topic modeling enables exploration and compact representation of a corpus.\nThe CaringBridge (CB) dataset is a massive collection of journals written by\npatients and caregivers during a health crisis. Topic modeling on the CB\ndataset, however, is challenging due to the asynchronous nature of multiple\nauthors writing about their health journeys. To overcome this challenge we\nintroduce the Dynamic Author-Persona topic model (DAP), a probabilistic\ngraphical model designed for temporal corpora with multiple authors. The\nnovelty of the DAP model lies in its representation of authors by a persona ---\nwhere personas capture the propensity to write about certain topics over time.\nFurther, we present a regularized variational inference algorithm, which we use\nto encourage the DAP model's personas to be distinct. Our results show\nsignificant improvements over competing topic models --- particularly after\nregularization, and highlight the DAP model's unique ability to capture common\njourneys shared by different authors.","url_abs":"http://arxiv.org/abs/1801.04958v1","url_pdf":"http://arxiv.org/pdf/1801.04958v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"topic-modeling-on-health-journals-with","repo_url":"https://github.com/robert-giaquinto/dynamic-author-persona-topic-model","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}