LabelTopics: Get some topic labels using a "more probable" method of terms
In ChengMengli/topic: Functions for Text Mining and Topic Modeling

Description Usage Arguments Value Examples

Function calls GetProbableTerms with some rules to get topic labels. This function is in "super-ultra-mega alpha"; use at your own risk/discretion.

1	LabelTopics(assignments, dtm, M = 2)

`assignments`	A documents by topics matrix similar to `theta`. This will work best if this matrix is sparse, with only a few non-zero topics per document.
`dtm`	A document term matrix of class `matrix` or `dgCMatrix`. The columns of `dtm` should be n-grams whose colnames have a "_" where spaces would be between the words.
`M`	The number of n-gram labels you want to return. Defaults to 2

Returns a matrix whose rows correspond to topics and whose j-th column corresponds to the j-th "best" label assignment.

# Load a pre-formatted dtm and topic model
data(nih_sample_dtm)
data(nih_sample_topic_model) 

assignments <- t(apply(nih_sample_topic_model$theta, 1, function(x){
  x[ x < 0.05 ] <- 0
  x / sum(x)
}))

labels <- LabelTopics(assignments = assignments, dtm = nih_sample_dtm, M = 2)