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The proposed model uses separate but associated hidden variables to explore both topics and individual interests such that it can provide useful insights to the participants of online healthcare chat groups about their interests in terms of weighted topics or vice versa.
We evaluate the proposed model on a real-world chat log by comparing its performance to benchmark topic models, i.e., latent Dirichlet allocation (LDA) and Author Topic Model (ATM), on the topic extraction task.