JSON.parse error: cannot see the plot

[sameiali]

Hi,

as a beginner in R and topic modelling with R, I am following what other people suggested to first fit a lda model on a corpus of corporate annual reports, and then visualize the results thorugh LDAvis. Everything works fine until the very last step, when I open the directory on the browser and get the following error:

"SyntaxError: JSON.parse: bad control character in string literal at line 10 column 16177 of the JSON data"

Here are my codes:

#load text mining library
library(tm)
#load files into corpus
#get listing of .txt files in directory
ceoletters <- read.csv("ceoletters.csv")
corpus <- iconv(ceoletters$ceoletter, to = "ASCII", sub = "")
#create corpus from vector
letters <- Corpus(VectorSource(corpus))
#start preprocessing
letters <-tm_map(letters,content_transformer(tolower))
letters <- tm_map(letters, removePunctuation)
letters <- tm_map(letters, removeNumbers)
letters <- tm_map(letters,removeWords,stopwords("english"))
letters <- tm_map(letters, stripWhitespace)
#Stem document
letters <- tm_map(letters,stemDocument)
#Create document-term matrix
dtm <- DocumentTermMatrix(letters)
#convert rownames to filenames
rownames(dtm) <- ceoletters$letter_id
#collapse matrix by summing over columns
freq <- colSums(as.matrix(dtm))
#length should be total number of terms
length(freq)
#create sort order (descending)
ord <- order(freq,decreasing=TRUE)
#List all terms in decreasing order of freq and write to disk
freq[ord]
write.csv(freq[ord],"word_freq.csv")

##fitting LDA
#load topic models library
library(topicmodels)
library(doParallel)
#Set parameters for Gibbs sampling
burnin <- 2000
iter <- 2000
thin <- 500
seed <-list(2003,5,63,100001,765)
nstart <- 5
best <- TRUE
registerDoParallel(4)
#Number of topics
k <- 100
#Run LDA using Gibbs sampling
ldaOut <-LDA(dtm,k, method="Gibbs", control=list(nstart=nstart, seed = seed, best=best, burnin = burnin, iter = iter, thin=thin))
#write out results
#docs to topics
ldaOut.topics <- as.matrix(topics(ldaOut))
write.csv(ldaOut.topics,file=paste("LDAGibbs",k,"DocsToTopics.csv"))
#top 20 terms in each topic
ldaOut.terms <- as.matrix(terms(ldaOut,10))
write.csv(ldaOut.terms,file=paste("LDAGibbs",k,"TopicsToTerms.csv"))
#probabilities associated with each topic assignment
topicProbabilities <- as.data.frame(ldaOut@gamma)
write.csv(topicProbabilities,file=paste("LDAGibbs",k,"TopicProbabilities.csv"))

and here are my codes to visualize the results:

library(LDAvis)
library(servr)
topicmodels2LDAvis <- function(x, ...){
  post <- topicmodels::posterior(x)
  if (ncol(post[["topics"]]) < 3) stop("The model must contain > 2 topics")
  mat <- x@wordassignments
  LDAvis::createJSON(
    phi = post[["terms"]], 
    theta = post[["topics"]],
    vocab = colnames(post[["terms"]]),
    doc.length = slam::row_sums(mat, na.rm = TRUE),
    term.frequency = slam::col_sums(mat, na.rm = TRUE)
  )
}
serVis(topicmodels2LDAvis(ldaOut))

Any idea to solve this problem?
Thanks.