R topic modeling: lda model labeling function - r

Question

I used LDA to build a topic model for 2 text documents say A and B. document A is highly related to say computer science and document B is highly related to say geo-science. Then I trained an lda using this command :
text<- c(A,B) # introduced above
r <- Corpus(VectorSource(text)) # create corpus object
r <- tm_map(r, tolower) # convert all text to lower case
r <- tm_map(r, removePunctuation)
r <- tm_map(r, removeNumbers)
r <- tm_map(r, removeWords, stopwords("english"))
r.dtm <- TermDocumentMatrix(r, control = list(minWordLength = 3))
my_lda <- LDA(r.dtm,2)
now i want to use my_lda to predict the context of a new document say C and i want to see if it is related to computer Science or geo-science. i know if i use this code for prediction
x<-C# a new document (a long string) introduced above for prediction
rp <- Corpus(VectorSource(x)) # create corpus object
rp <- tm_map(rp, tolower) # convert all text to lower case
rp <- tm_map(rp, removePunctuation)
rp <- tm_map(rp, removeNumbers)
rp <- tm_map(rp, removeWords, stopwords("english"))
rp.dtm <- TermDocumentMatrix(rp, control = list(minWordLength = 3))
test.topics <- posterior(my_lda,rp.dtm)
It will give me a label 1 or 2 and I don't have any idea what 1 or 2 represents... How can I realize if it means computer science related or geo-science related?

Answer 1

You can extract the most likely terms from your LDA topicmodel and replace those black-box numeric names with however many of them you would like. Your example isn't reproducible, but here is example illustrating how you can do this:
> library(topicmodels)
> data(AssociatedPress)
>
> train <- AssociatedPress[1:100]
> test <- AssociatedPress[101:150]
>
> train.lda <- LDA(train,2)
>
> #returns those black box names
> test.topics <- posterior(train.lda,test)$topics
> head(test.topics)
1 2
[1,] 0.57245696 0.427543038
[2,] 0.56281568 0.437184320
[3,] 0.99486888 0.005131122
[4,] 0.45298547 0.547014530
[5,] 0.72006712 0.279932882
[6,] 0.03164725 0.968352746
> #extract top 5 terms for each topic and assign as variable names
> colnames(test.topics) <- apply(terms(train.lda,5),2,paste,collapse=",")
> head(test.topics)
percent,year,i,new,last new,people,i,soviet,states
[1,] 0.57245696 0.427543038
[2,] 0.56281568 0.437184320
[3,] 0.99486888 0.005131122
[4,] 0.45298547 0.547014530
[5,] 0.72006712 0.279932882
[6,] 0.03164725 0.968352746
> #round to one topic if you'd prefer
> test.topics <- apply(test.topics,1,function(x) colnames(test.topics)[which.max(x)])
> head(test.topics)
[1] "percent,year,i,new,last" "percent,year,i,new,last" "percent,year,i,new,last"
[4] "new,people,i,soviet,states" "percent,year,i,new,last" "new,people,i,soviet,states"