Detecting short-term cyclical topic dynamics in the user-generated content and news
We propose the PDHA model to capture short-term topic cyclical dynamics. We developed an augmented Gibbs sampling approach for model inference. We evaluated performance using text data from the user generated content and news. PDHA is able to capture and characterize intraday and longer topic cyclical dynamics.
cyclical dynamics gibbs sampling topic models temporal dynamics context dependent