The following are word clouds of tweets for each candidate from the October 16, 2012 debate with the bigger words the more often they were used in tweets (click on each word cloud to enlarge):

And the net-negative posts for each candidate:

Please note that the bigger the word is in the word cloud the more often it was used.

The R code for creating the word clouds

The following code was adapted from here and is an extension of previous work:

ap.corpus <- Corpus(DataframeSource(data.frame(as.character(romneypositive[,3])))) ap.corpus <- tm_map(ap.corpus, removePunctuation) ap.corpus <- tm_map(ap.corpus, tolower) ap.corpus <- tm_map(ap.corpus, function(x) removeWords(x, c(r,stopwords("english")))) ap.tdm <- TermDocumentMatrix(ap.corpus) ap.m <- as.matrix(ap.tdm) ap.v <- sort(rowSums(ap.m),decreasing=TRUE) ap.d <- data.frame(word = names(ap.v),freq=ap.v) table(ap.d$freq) pal2 <- brewer.pal(8,"Dark2") png("romneypositive.png", width=1280,height=800) wordcloud(ap.d$word,ap.d$freq, scale=c(8,.2),min.freq=3, max.words=Inf, random.order=FALSE, rot.per=.15, colors=pal2) dev.off()

Disclaimer: Any errors can be attributed to the fact that I was drinking heavily, that I was in Dallas, and that it was half four in the morning when I finished writing this.