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Install necessary packages

# install.packages('tm')
# install.packages('RColorBrewer')
# install.packages('wordcloud')
library('tm')
## Loading required package: NLP
library('RColorBrewer')
library('wordcloud')

Collect tweets from Twitter API

## [1] "Using direct authentication"

Process data

# Import data from a csv file to data frame 
liuBK <- read.csv("liuBKData.csv", comment.char="#")
LIUTweets <- subset(liuBK)

# Save and read data to/from a R data object
saveRDS(LIUTweets, "LIUTweets.RDS")
LIUTweets <- readRDS("LIUTweets.RDS")

MLIUTweets <- LIUTweets$text

# Function to clean tweets
clean.text = function(x)
{
  # tolower
  x = tolower(x)
  # remove rt
  x = gsub("rt", "", x)
  # remove at
  x = gsub("@\\w+", "", x)
  # remove punctuation
  x = gsub("[[:punct:]]", "", x)
  # remove numbers
  x = gsub("[[:digit:]]", "", x)
  # remove links http
  x = gsub("http\\w+", "", x)
  # remove tabs
  x = gsub("[ |\t]{2,}", "", x)
  # remove blank spaces at the beginning
  x = gsub("^ ", "", x)
  # remove blank spaces at the end
  x = gsub(" $", "", x)
  return(x)
}

# clean tweets
MLIUTweets = clean.text(MLIUTweets)

Create word cloud of tweets from LIU-Brooklyn

corpus = Corpus(VectorSource(MLIUTweets))
# corpus = Corpus(VectorSource(cmail))
# create term-document matrix
tdm = TermDocumentMatrix(
  corpus,
  control = list(
    wordLengths=c(3,20),
    removePunctuation = TRUE,
    stopwords = c("the", "a", stopwords("english")),
    removeNumbers = TRUE, 
    tolower = TRUE) )

# convert as matrix
tdm = as.matrix(tdm)

# get word counts in decreasing order
word_freqs = sort(rowSums(tdm), decreasing=TRUE) 

#remove the top words which don’t generate insights such as "the", "a", "and", etc.
#Here “1” is 1st word in the list we want to remove 
word_freqs = word_freqs[-(1:9)]  

# create a data frame with words and their frequencies
dm = data.frame(word=names(word_freqs), freq=word_freqs)

#Plot corpus in a clored graph; need RColorBrewer package
wordcloud(head(dm$word, 50), head(dm$freq, 50), random.order=FALSE, colors=brewer.pal(8, "Dark2"))

#check top 50 most mentioned words
head(word_freqs, 20)
##          students    eduaubdedububa      eduaubdedubu              glbs 
##                 9                 8                 8                 7 
##    ubliubrooklynu          ufufefpm           gameday              ufpm 
##                 7                 7                 6                 6 
##           francis       advocacyday            albany standupstudentaid 
##                 6                 5                 5                 5 
##  weareliubrooklyn               big           onepack             saint 
##                 5                 5                 5                 5 
##              wrac   blackbirdnation         spotlight        blackbirds 
##                 5                 5                 4                 4