swoon_chat_sentiment.R

# required pakacges
setwd('/Users/shill/documents/R scripts')
library("XML")
library(twitteR)
library(sentiment)
library(plyr)
library(ggplot2)
library(wordcloud)
library(RColorBrewer)

reqURL <- "https://api.twitter.com/oauth/request_token"
accessURL <- "http://api.twitter.com/oauth/access_token"
authURL <- "http://api.twitter.com/oauth/authorize"
consumerKey <- "v9Cj7tF5EkTf4ePAQNExg"
consumerSecret <- "pv3H5henMHmCetABqWMW4vyiUUFvqWteE9q853grs"
#twitCred <- OAuthFactory$new(consumerKey=consumerKey,
#                             consumerSecret=consumerSecret,
#                             requestURL=reqURL,
#                             accessURL=accessURL,
#                             authURL=authURL)
#twitCred$handshake()
load("twitCred.sav")
registerTwitterOAuth(twitCred)

## GET SWOON MESSAGE DATA
docs <- "/Users/shill/Desktop/SMM/feed.xml"
docdata<-xmlToDataFrame(docs) #my xml file
some_txt<-as.character(docdata$description[1:6000]) ##truncate


# remove retweet entities
some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", some_txt)
# remove at people
some_txt = gsub("@\\w+", "", some_txt)
# remove punctuation
some_txt = gsub("[[:punct:]]", "", some_txt)
# remove numbers
some_txt = gsub("[[:digit:]]", "", some_txt)
# remove html links
some_txt = gsub("http\\w+", "", some_txt)
# remove unnecessary spaces
some_txt = gsub("[ \t]{2,}", "", some_txt)
some_txt = gsub("^\\s+|\\s+$", "", some_txt)

# define "tolower error handling" function 
try.error = function(x)
{
  # create missing value
  y = NA
  # tryCatch error
  try_error = tryCatch(tolower(x), error=function(e) e)
  # if not an error
  if (!inherits(try_error, "error"))
    y = tolower(x)
  # result
  return(y)
}
# lower case using try.error with sapply 
some_txt = sapply(some_txt, try.error)

# remove NAs in some_txt
some_txt = some_txt[!is.na(some_txt)]
names(some_txt) = NULL

# classify emotion - anger, disgust, fear, joy, sadness, and surprise
class_emo = classify_emotion(some_txt, algorithm="bayes", prior=1.0)
# get emotion best fit
emotion = class_emo[,7]
# substitute NA's by "unknown"
emotion[is.na(emotion)] = "unknown"

# classify polarity
class_pol = classify_polarity(some_txt, algorithm="bayes")
# get polarity best fit
polarity = class_pol[,4]

# data frame with results
sent_df = data.frame(text=some_txt, emotion=emotion,
                     polarity=polarity, stringsAsFactors=FALSE)

# sort data frame
sent_df = within(sent_df,
                 emotion <- factor(emotion, levels=names(sort(table(emotion), decreasing=TRUE))))

# plot distribution of emotions
ggplot(sent_df, aes(x=emotion)) +
  geom_bar(aes(y=..count.., fill=emotion)) +
  scale_fill_brewer(palette="Dark2") +
  labs(x="emotion categories", y="number of conversations") +
  opts(title = "Sentiment Analysis of Conversations on Swoon\n(classification by emotion)",
       plot.title = theme_text(size=12))

# plot distribution of polarity
ggplot(sent_df, aes(x=polarity)) +
  geom_bar(aes(y=..count.., fill=polarity)) +
  scale_fill_brewer(palette="RdGy") +
  labs(x="polarity categories", y="number of conversatins") +
  opts(title = "Sentiment Analysis of Conversations on Swoon\n(classification by polarity)",
       plot.title = theme_text(size=12))

# separating text by emotion
emos = levels(factor(sent_df$emotion))
nemo = length(emos)
emo.docs = rep("", nemo)
for (i in 1:nemo)
{
  tmp = some_txt[emotion == emos[i]]
  emo.docs[i] = paste(tmp, collapse=" ")
}

# remove stopwords
emo.docs = removeWords(emo.docs, stopwords("english"))
# create corpus
corpus = Corpus(VectorSource(emo.docs))
tdm = TermDocumentMatrix(corpus)
tdm = as.matrix(tdm)
colnames(tdm) = emos

# comparison word cloud
comparison.cloud(tdm, colors = brewer.pal(nemo, "Dark2"),
                 scale = c(3,.5), random.order = FALSE, title.size = 1.5)