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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"?

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