Optimize the hyper-parameters for LDA

perplexity() computes the perplexity score to help users to chose the optimal values of hyper-parameters for LDA.

Usage

perplexity(x, newdata = NULL, ...)

Arguments

x

a LDA model fitted by textmodel_seededlda() or textmodel_lda().

newdata

if provided, theta and phi are estimated through fresh Gibbs sampling.

...

additional arguments passed to textmodel_lda.

Value

Returns a singple numeric value.

Details

perplexity() predicts the distribution of words in the dfm based on x$alpha and x$gamma and then compute the sum of disparity between their predicted and observed frequencies. The perplexity score minimizes when the chosen values of hyper-parameters such as k, alpha and gamma are optimal.

See also

divergence