This is finished by applying a simple topic modeling method called Latent Dirichlet Allocation , . Topic models are graphical fashions which assume that shared international multinomial word distributions (i.e., subject distributions) govern the corpus. Word frequencies within a given document are created from a mixture of those world subject distributions. LDA is the simplest topic model and uses the Dirichlet prior so as to ensure sparsity in the underlying multinomial distribution. Since LDA has already yielded glorious outcomes, we did not discover it useful to make use of extra elaborate topic fashions.
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