This package implements a collapsed Gibbs Sampler algorithm to fit a topic model to a set of unstructured text documents. It contains three basic groups of functions: (1) pre-processing of unstructured text, including substitutions, tokenization, and stemming, (2) fitting the Latent Dirichlet Allocation (LDA) topic model to training data and making model-based predictions on test data, and (3) visualizing and summarizing the fitted model.
Package details |
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Maintainer | |
License | |
Version | 0.1 |
Package repository | View on GitHub |
Installation |
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