Predicting LDA topics for new data

Question

It looks like this question has may have been asked a few times before (here and here), but it has yet to be answered. I'm hoping this is due to the previous ambiguity of the question(s) asked, as indicated by comments. I apologize if I am breaking protocol by asking a simliar question again, I just assumed that those questions would not be seeing any new answers.

Anyway, I am new to Latent Dirichlet Allocation and am exploring its use as a means of dimension reduction for textual data. Ultimately I would like extract a smaller set of topics from a very large bag of words and build a classification model using those topics as a few variables in the model. I've had success in running LDA on a training set, but the problem I am having is being able to predict which of those same topics appear in some other test set of data. I am using R's topicmodels package right now, but if there is another way to this using some other package I am open to that as well.

Here is an example of what I am trying to do:

library(topicmodels)
data(AssociatedPress)

train <- AssociatedPress[1:100]
test <- AssociatedPress[101:150]

train.lda <- LDA(train,5)
topics(train.lda)

#how can I predict the most likely topic(s) from "train.lda" for each document in "test"?

Answer 1

With the help of Ben's superior document reading skills, I believe this is possible using the posterior() function.

library(topicmodels)
data(AssociatedPress)

train <- AssociatedPress[1:100]
test <- AssociatedPress[101:150]

train.lda <- LDA(train,5)
(train.topics <- topics(train.lda))
#  [1] 4 5 5 1 2 3 1 2 1 2 1 3 2 3 3 2 2 5 3 4 5 3 1 2 3 1 4 4 2 5 3 2 4 5 1 5 4 3 1 3 4 3 2 1 4 2 4 3 1 2 4 3 1 1 4 4 5
# [58] 3 5 3 3 5 3 2 3 4 4 3 4 5 1 2 3 4 3 5 5 3 1 2 5 5 3 1 4 2 3 1 3 2 5 4 5 5 1 1 1 4 4 3

test.topics <- posterior(train.lda,test)
(test.topics <- apply(test.topics$topics, 1, which.max))
#  [1] 3 5 5 5 2 4 5 4 2 2 3 1 3 3 2 4 3 1 5 3 5 3 1 2 2 3 4 1 2 2 4 4 3 3 5 5 5 2 2 5 2 3 2 3 3 5 5 1 2 2