As may or may not be evident from the question, I'm pretty new to R and I could do with a bit of help on this.

When creating topic models, I've experimented with LDA and LDAvis - code in (A) and (B) below. LDA in (A) allows me to find the posterior probability of the topics occurring in each document within my corpus, which I have used to run regressions with variables from other datasets. (B), the topic generation approach using LDAvis, generates 'better', more coherent topics than through (A), but I haven't been able to work out how to find the posterior probabilities of the topics occurring in a given document with the LDAvis approach, or whether to discount this as an impossible task.

All advice greatly appreciated.

Thank you!

**(A)**

```
set.seed(1)
require(topicmodels)
set.seed(1)
P5LDA4 <- LDA(P592dfm, control=list(seed=1), k = 23)
set.seed(1)
terms(P5LDA4, k =30)
#find posterior probability
postTopics <- data.frame(posterior(P5LDA4)$topics)
postTopics
```

**(B)**

```
# MCMC and model tuning parameters:
K <- 23
G <- 5000
alpha <- 0.02
eta <- 0.02
# convert to lda format
dfmlda <- convert(newdfm, to = "lda")
# fit the model
library(lda)
set.seed(1)
t1 <- Sys.time()
fit <- lda.collapsed.gibbs.sampler(documents = dfmlda$documents, K = K,
vocab = dfmlda$vocab,
num.iterations = G, alpha = alpha,
eta = eta, initial = NULL, burnin = 0,
compute.log.likelihood = TRUE)
t2 <- Sys.time()
t2 - t1
#Time difference of 3.13337 mins
save(fit, file = "./fit.RData")
load("./fit.RData")
library(LDAvis)
set.seed(1)
json <- createJSON(phi = t(apply(t(fit$topics) + eta, 2, function(x) x/sum(x))),
theta = t(apply(fit$document_sums + alpha, 2, function(x) x/sum(x))),
doc.length = ntoken(newdfm),
vocab = features(newdfm),
term.frequency = colSums(newdfm))
serVis(json, out.dir = "./visColl", open.browser = TRUE)
```