document: Online multiscale dynamic topic models We propose an online topic model for sequentially analyzing the time evolution of topics in document collections . Topics naturally evolve with multiple timescales . For example , some words may be used consistently over one hundred years , while other words emerge and disappear over periods of a few days . Thus , in the proposed model , current topic-specific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch . Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model . We derive efficient online inference procedures based on a stochastic EM algorithm , in which the model is sequentially updated using newly obtained data ; this means that past data are not required to make the inference . We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps .
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