library(twitteR)
setup_twitter_oauth("PlCtxAuNAfWdYwBjtdWk8NDAy","uNfrYZAkS8vQhD9GXOr43mIYM3LE6FDWzS4zQdPafAMLwSbVPw","118065065-hP0YCXBb86irtPuaEcBhJKIVxQDKDckZPxJv5oxK","gwrf2gXs9GgF3N3X0V3P7OfNjJtPYX8BaXV9F3TmIuhFs")
## [1] "Using direct authentication"
rdmTweets <- userTimeline("cia", n=500)
rdmTweets[1:3]
## [[1]]
## [1] "CIA: CIA #Museum Artifact of the Week: Afghan Hat\nA gift from Afghan President Karzai to former DCI George Tenet<U+0085> https://t.co/zUAZ9OqMQB"
## 
## [[2]]
## [1] "CIA: ICYMI:\nNew Anthology: \nCIA &amp; the Wars in Southeast Asia, 1947-75\n\n41 #unclassified articles &amp; more!<U+0085> https://t.co/ukmH3tTIoY"
## 
## [[3]]
## [1] "CIA: ICYMI:\nNew #Unclassified \"Studies in Intel\":\n-Intel for Warfighter\n-Why Bad Things Happen to Good Analysts\n-&amp; more!<U+0085> https://t.co/pNPryg92u1"
df <- do.call("rbind", lapply(rdmTweets, as.data.frame))
library(tm)
## Loading required package: NLP
Corpus1=Corpus(VectorSource(df$text))
Corpus1 <- tm_map(Corpus1, removePunctuation)  
Corpus1 <- tm_map(Corpus1, removeNumbers)  
Corpus1 <- tm_map(Corpus1, tolower)
Corpus1 <- tm_map(Corpus1, removeWords, stopwords("english"))
Corpus1 <- tm_map(Corpus1, stemDocument)  
Corpus1 <- tm_map(Corpus1, stripWhitespace)   
Corpus1 <- tm_map(Corpus1, PlainTextDocument)
dtm <- DocumentTermMatrix(Corpus1)
tdm <- TermDocumentMatrix(Corpus1)
matx1=as.matrix(tdm)
sort1=sort(rowSums(matx1),decreasing=T)
di=data.frame(Word=names(sort1),Frequency=sort1)
library(wordcloud)
## Loading required package: RColorBrewer
wordcloud(di$Word, di$Frequency, max.words=100,colors=brewer.pal(6, "Set1"))   

findFreqTerms(dtm, lowfreq=10)
##  [1] "amp"        "artifact"   "cia"        "icymi"      "inmemoriam"
##  [6] "intel"      "intelcon"   "museum"     "oss"        "pdb"       
## [11] "week"
findAssocs(dtm, 'usa', 0.30)
## $usa
## numeric(0)