`R/topic_modeling_utilities.R`

`LabelTopics.Rd`

Function calls `GetProbableTerms`

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

`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.

```
# make a dtm with unigrams and bigrams
data(nih_sample_topic_model)
m <- nih_sample_topic_model
assignments <- t(apply(m$theta, 1, function(x){
x[ x < 0.05 ] <- 0
x / sum(x)
}))
assignments[is.na(assignments)] <- 0
labels <- LabelTopics(assignments = assignments, dtm = m$data, M = 2)
#> dtm does not appear to contain ngrams. Using unigrams but ngrams will work much better.
```