Topic Modeling issue in R

I am getting the following error when running the code that follows, it would very kind if someone help me on this

Warning in gsub(sprintf(“(*UCP)b(%s)b”, paste(sort(words, decreasing = TRUE),): PCRE pattern compilation error ‘quantifier does not follow a repeatable item’ at ‘?T |fig|far|ever|even|eufbect|etc|entir|enough|end|earli|draw|diufber|deufbn|day|come|can|brief|brief|bit|away|awar|attempt|ask|anoth|andrn |also|achiev|abl|aalborg|â???o|â???o|â???|’ve)b’ Error in gsub(sprintf(“(*UCP)b(%s)) b”, paste(sort(words, decreasing = TRUE), : invalid regular expression ‘(*UCP)b(yes|will|well|way|want|vol|use|universitet|ufbrst|ufbrm|ufbnd|two| tion|though|think|thing|thern|tend|tell|take|sure|speciufbc|someth|sinc|signiufbc|set|seen|see|say|said|re |rather|quit|put|ptrn |proufbt|post, look,right|point|particular|one|often|ofrn|octob|now|new|never|need|much|moreov|might|may|make|lot|littl|like|let|last|larg|lack|kind| journal|howev|help|great|good|get|found|follow|first|firmâ???T|fig|far|ever|even|eufbect|etc|entir|enough|end|earli|draw|diufber|deufbn| day|come|can|brief|brief|bit|away|awar|attempt|ask|a noth|andrn|also|achiev|abl|aalborg|â???o|â???o|â???|’ve)b’

title: “Text Mining” output: html_document

knitr::opts_chunk$set(echo = TRUE)

#Load Libraries


#Get and load PDF’s

files <- list.files(path = "C:\Users\cba", pattern = "pdf$", include.dirs = TRUE)

Rpdf <- readPDF(control = list(text = "-layout"))

###Remember to manually set working directory
documents <- lapply(files, pdf_text)  #%>% read_lines())

corp <- Corpus(VectorSource(documents))

##Clean text
corp <-tm_map(corp,content_transformer(tolower))

#remove punctuation
corp <- tm_map(corp, removePunctuation)
#Strip digits
corp <- tm_map(corp, removeNumbers)
#remove stopwords
corp <- tm_map(corp, removeWords, stopwords("english"))
#remove whitespace
corp <- tm_map(corp, stripWhitespace)
#Remove URL
urlPat<-function(x) gsub("(ftp|http)(s?)://.*\b", "", x)
corp<-tm_map(corp, urlPat)
#Remove Email
emlPat<-function(x) gsub("\b[A-Z a-z 0-9._ - ]*[@](.*?)[.]{1,3} \b", "", x)
corp<- tm_map(corp, emlPat)

#Stem document
corp <- tm_map(corp,stemDocument)

#Remove stopwords
myStopwords <- c("can", "say","one","way","use",
"post,look,right" , "now","think","'ve",
"re ","anoth","put","set","new","good",
"great","lot","fig", "let", "follow", "ptrn
", "aalborg", "universitet", "â???o", "proufbt", "ufbrm", "eufbect", "diufber", "ufbrst", "deufbn", "speciufbc", "signiufbc", "ufbnd", "â???", "firmâ???T", "â???o", "vol", "thern", "ofrn", "journal", "tion", "andrn", "octob" )
corp <- tm_map(corp, removeWords, myStopwords)
#inspect a document as a check
#Good practice to check every now and then

#Convert to document matrix
dtm <- DocumentTermMatrix(corp)
#remove sparse words
dtm <- removeSparseTerms(dtm,0.99)

#Alternative way to check words for several documents:

#Run LDA

#convert rownames to filenames
rownames(dtm) <- files
#collapse matrix by summing over columns
freq <- colSums(as.matrix(dtm))
#length should be total number of terms
#create sort order (descending)
ord <- order(freq,decreasing=TRUE)
#List all terms in decreasing order of freq and write to disk

#Set parameters for Gibbs sampling
burnin <- 4000 
iter <- 4000
thin <- 500
seed <-list(3024,547,67,100000457,844)
nstart <- 5
keep <- 50
best <- TRUE

#For loop choosing different number of topics
#Currently selected 20 topics
for (index in 1:1) {
#Number of topics
if(index == 1) {k=20} else if (index==2) {k=50} else if (index==3) {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))

kable(ldaOut.topics) %>%
  kable_styling(bootstrap_options = c("condensed", "hover", "striped"), full_width = F, position ="center" )

#top 6 terms in each topic

ldaOut.terms <- as.matrix(terms(ldaOut,10))
 kable(ldaOut.terms) %>%
kable_styling(bootstrap_options = c("condensed", "hover", "striped"), full_width = F, position ="center" )

#probabilities associated with each topic assignment
 #add names to prop table
 rownames(ldaOut@gamma) <- files
 topicProbabilities <-
 kable(topicProbabilities, rownames =files) %>%
  kable_styling(bootstrap_options = c("condensed", "hover", "striped"), full_width = F, position ="center")  

 #Find relative importance of top 2 topics
topic1ToTopic2 <- lapply(1:nrow(dtm),function(x)
kable(topic1ToTopic2) %>%
  kable_styling(bootstrap_options = c("condensed", "hover", "striped"), full_width = F, position ="center" )

#Find relative importance of second and third most important topics
topic2ToTopic3 <- lapply(1:nrow(dtm),function(x)
kable(topic1ToTopic2) %>%
  kable_styling(bootstrap_options = c("condensed", "hover", "striped"), full_width = F, position ="center" )
#manually set working directory, to specify where the files should be saved.
setwd("C:\Users\cba\OneDrive - Aalborg Universitet\Papers\Litterature Review\Excel Filer")
if(index==1) {write.csv(files, file="document names.csv")}
write.csv(as.matrix(unlist(topic1ToTopic2)),file=paste("Topics", k, "Topic1ToTopic2.csv"))
write.csv(ldaOut.terms,file=paste("Topics", k, "Topic Overview.csv"))
write.csv(as.matrix(unlist(topic2ToTopic3)),file=paste("Topics", k,"Topic2ToTopic3.csv"))
write.csv(topicProbabilities,file=paste("Topics", k,"topicProbabilities.csv"))
write.csv(unlist(ldaOut.terms),file=paste("Topics", k,"Top10Words.csv"))
} # end for loop

#Cross validation

n <- nrow(dtm)

#Create training and test dataset
#in this case 75% is in the training set and 25% in the testset
splitter <- sample(1:n, round(n * 0.75))
train_set <- dtm[splitter, ]
test_set <- dtm[-splitter, ]

#----------------5-fold cross-validation, different numbers of topics----------------
#Use multiple cores for faster runtime
cluster <- makeCluster(detectCores(logical = TRUE) - 1) # leave one CPU spare...

clusterEvalQ(cluster, {

#select parameters for cross validation
burnin <- 4000 
iter <- 4000
thin <- 500
seed <-list(3024,547,67,100000457,844)
keep <- 50
best <- TRUE
folds <- 5
splitfolds <- sample(1:folds, n, replace = TRUE)
candidate_k <- c(100,200) #c(2, 3, 4, 5, 10, 20, 30, 40, 50, 75) #, 100, 200, 300 candidates for how many topics
clusterExport(cluster, c("dtm", "burnin", "iter", "keep", "splitfolds", "folds", "candidate_k", "LDA"))

# we parallelize by the different number of topics.  A processor is allocated a value
# of k, and does the cross-validation serially.  This is because it is assumed there
# are more candidate values of k than there are cross-validation folds, hence it
# will be more efficient to parallelise
results <- foreach(j = 1:length(candidate_k), .combine = rbind) %dopar%{
   k <- candidate_k[j]
   results_1k <- matrix(0, nrow = folds, ncol = 2)
   colnames(results_1k) <- c("k", "perplexity")
   for(i in 1:folds){
      train_set <- dtm[splitfolds != i , ]
      test_set <- dtm[splitfolds == i, ]
      fitted <- LDA(train_set, k = k, method = "Gibbs",
                    control = list(burnin = burnin, iter = iter, keep=keep ) )
      results_1k[i,] <- c(k, perplexity(fitted, newdata = test_set))

results_df <-

#Export results to csv
write.csv(results_df,file=paste("cluster analysis.csv"))

ggplot(results_df, aes(x = k, y = perplexity)) +
   geom_point() +
   geom_smooth(se = FALSE) +
   ggtitle("5-fold cross-validation of topic modelling ",
           "(ie five different models fit for each candidate number of topics)") +
   labs(x = "Candidate number of topics", y = "Perplexity")

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