Text Mining on Large Dataset
You are using R, and everything that you are currently working on will be held in memory hence the error. You just don't have enough of it. 

You might be better off creating the frequencies of the terms from the original splits as opposed to creating one big file. 

Then after you have the frequencies adding them all together. 

Personally I use this code to create my wordclouds.

##Clean code for single column of a dataframe, in this case named Text
  x = alltweets
  tweets.text &lt;- x$text
  tweets.text.cleaned &lt;- gsub("@\\w+ *#", "", tweets.text)
  tweets.text.cleaned &lt;- gsub("(f|ht)tp(s?)://(.*)[.][a-z]+", "", tweets.text.cleaned)
  tweets.text.cleaned &lt;- gsub("[^0-9A-Za-z///' ]", "", tweets.text.cleaned)
  tweets.text.corpus &lt;- Corpus(VectorSource(tweets.text.cleaned))
  tweets.text.final &lt;- tm_map (tweets.text.corpus, removePunctuation, mc.cores=1)
  tweets.text.final2 &lt;- tm_map (tweets.text.final, content_transformer(tolower), mc.cores=1)
  tweets.text.final2 &lt;- tm_map(tweets.text.final2, removeNumbers, mc.cores=1)
  tweets.text.final2 &lt;- tm_map(tweets.text.final2, removePunctuation, mc.cores=1)
  tweets.text.final2 &lt;- tm_map(tweets.text.final2,removeWords, stopwords("English"), mc.cores=1)
  tweets.text.final2 = tm_map(tweets.text.final2, removeWords, c("amp", "&amp;"))
#create corpus
   housing.tweets.corpus &lt;- Corpus(VectorSource(tweets.text))

#clean up by removing stop words
   housing.tweets.corpus &lt;- tm_map(housing.tweets.corpus, function(x)removeWords(x,stopwords()))

#install wordcloud if not already installed
   install.packages("wordcloud")
   library("wordcloud")

#generate wordcloud
   wordcloud(housing.tweets.corpus,min.freq = 2, scale=c(7,0.5),colors=brewer.pal(8, "Dark2"),  random.color= TRUE, random.order = FALSE, max.words = 500)