(This article was first published on Decisions and R, and kindly contributed to R-bloggers)
I've been exploring Jeff Gentry's twitteR package, which has a ton of great functionality for intereacting with twitter data in R. Today, I thought a bit about a problem I've noticed several times on twitter: users profiles are often only noisy signals of the content they tweet about!
I decided that a table of a user's commonly-used tweets might give a better sense of the content a user tweets about. My code to extract the hashtags is below (note: you'll need to load the twitteR package, and complete the OAuth Authentication first.. if you're having trouble with this, try visiting this page)
Here's the code I used:
tw = userTimeline("BarackObama", cainfo = x1, n = 3200)
tw = twListToDF(tw)
vec1 = tw$text
extract.hashes = function(vec){
hash.pattern = "#[[:alpha:]]+"
have.hash = grep(x = vec, pattern = hash.pattern)
hash.matches = gregexpr(pattern = hash.pattern,
text = vec[have.hash])
extracted.hash = regmatches(x = vec[have.hash], m = hash.matches)
df = data.frame(table(tolower(unlist(extracted.hash))))
colnames(df) = c("tag","freq")
df = df[order(df$freq,decreasing = TRUE),]
return(df)
}
dat = head(extract.hashes(vec1),50)
dat2 = transform(dat,tag = reorder(tag,freq))
library(ggplot2)
p = ggplot(dat2, aes(x = tag, y = freq)) + geom_bar(fill = "blue")
p + coord_flip() + labs(title = "Hashtag frequencies in the tweets of the Obama team (@BarackObama)")
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