library(tm)
library(topicmodels)
lda_topicmodel <- model_LDA(dtm, k=20, control=list(seed=1234))
I performed Latent Dirichlet Allocation using the LDA
function in R. Now, I have an LDA
in the S4
object format.
How do I convert it to a word-topic matrix and a document-topic matrix in R?
Unfortunately, object of type 'S4' is not subsettable. So, I had to resort to copying a subset of the data for use.
Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7 Topic 8 Topic 9 Topic 10
[1,] "flooding" "beach" "sets" "flooding" "storm" "fwy" "storms" "flooding" "socal" "rain"
[2,] "erosion" "long" "alltime" "just" "flooding" "due" "thunderstorms" "via" "major" "california"
[3,] "cause" "abc7" "rain" "almost" "years" "closures" "flash" "public" "throughout" "nearly"
[4,] "emergency" "day" "slides" "hardcore" "mudslides" "avoid" "continue" "asks" "abc7" "southern"
[5,] "highway" "history" "last" "spun" "snow" "latest" "possible" "call" "streets" "storms"
Topic 11 Topic 12 Topic 13 Topic 14 Topic 15 Topic 16 Topic 17 Topic 18 Topic 19 Topic 20
[1,] "abc7" "abc7" "like" "widespread" "widespread" "across" "rainfall" "flooding" "flooding" "vehicles"
[2,] "beach" "flooding" "closed" "batters" "biggest" "can" "record" "region" "storm" "several"
[3,] "long" "stranded" "live" "california" "evacuations" "stay" "breaks" "reported" "california" "getting"
[4,] "fwy" "county" "raining" "evacuations" "mudslides" "home" "long" "corona" "causes" "floodwaters"
[5,] "710" "san" "blog" "mudslides" "years" "wires" "beach" "across" "related" "stranded"
The picture contains a subset of the words in each topic: LDA word-topic I wish to write the contents of the S4 object to a csv file like a word-topic matrix as shown: Word-Topic Matrix