Dynamic topic models are generative models in natural language processing that analyze the evolution of latent topics within a collection of documents over time. This family of topic models was proposed by David Blei and John Lafferty and was initially an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents.
Dynamic topic models are generative models in natural language processing that analyze the evolution of latent topics within a collection of documents over time. This family of topic models was proposed by David Blei and John Lafferty and was initially an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents.
Source: Wikipedia "Dynamic topic model" · CC BY-SA 4.0
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