Experiments with LDA and Top2Vec for embedded topic discovery on social media data-A case study of cystic fibrosis.

ABSTRACT: Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with

SUBMITTER: Karas B 

PROVIDER: S-EPMC9433987 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature