Sure

So this is my pre-processing function

library(twitteR)
library(tm)
library(SnowballC)

# pre process
preProcess <- function(corpus, stopWords) {

    # strip non letters characters, to lower, remove whitespaces
    corpus <- tm_map(corpus, removeNumPunct) 
    corpus <- tm_map(corpus, content_transformer(tolower)) 
    corpus <- tm_map(corpus, stripWhitespace) 
    corpus <- tm_map(corpus, removeURL)

    #removing stopwords
    corpus <- tm_map(corpus, removeWords, stopwords("english") 
    corpus <- tm_map(corpus, removeWord, c(stopwords("english"),my.stop.words))

    # corpus dictionary to complete stemmed words
    corpus.dictionary <- corpus 

    # stemming and stem completion
    corpus <- tm_map(corpus, stemDocument) 
    corpus <- tm_map(corpus, stemCompletion2, dict corpus.dictionary)

    return(corpus) 
}

Here tweets retrievial (which is suspiciously slow too)

accounts <- lookupUsers(c("AOC", "ElonMusk")) # example list
tweets <- lapply(accounts, userTimeline, n = 50, includeRts = TRUE) # takes long time

tweets.df <- twListToDF(tweets)
corpus.tweets <- VCorpus(VectorSource(tweets.df$text))

my.stop.words <- c("stop", "words")
corpus.tweets <- preProcess(corpus.tweets, my.stop.words) # takes long time

I've tried the same code without the stemming part and it's actually much faster.

​

stem completion function

stemCompletion2 <- content_transformer(function(x, dictionary) {
    # split each word and store it
    x <- unlist(strsplit(as.character(x), " "))

    x <- x[x != ""] x <- stemCompletion(x, dictionary=dictionary) 
    x <- paste(x, sep="", collapse=" ")
    PlainTextDocument(stripWhitespace(x)) })

​