Dynamic topic models

ABSTRACT

A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.

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Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Factorization methods

        • Canonical correlation analysis

• Mathematics of computing

  • Probability and statistics

    • Statistical paradigms

      • Regression analysis

      • Statistical graphics