sentiment_cloud.r

library(twitteR)
library(RCurl)
library(RJSONIO)
library(stringr)
library(tm)
library(wordcloud)


####################################################################

getSentiment <- function (text, key){
 
text <- URLencode(text);
 
#save all the spaces, then get rid of the weird characters that break the API, then convert back the URL-encoded spaces.
text <- str_replace_all(text, "%20", " ");
text <- str_replace_all(text, "%\\d\\d", "");
text <- str_replace_all(text, " ", "%20");
 
 
if (str_length(text) > 360){
text <- substr(text, 0, 359);
}
##########################################
 
data <- getURL(paste("http://api.datumbox.com/1.0/TwitterSentimentAnalysis.json?api_key=", key, "&text=",text, sep=""))
 
js <- fromJSON(data, asText=TRUE);
 
# get mood probability
sentiment = js$output$result
 
###################################

 
return(list(sentiment=sentiment))
}

clean.text <- function(some_txt)
{
some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", some_txt)
some_txt = gsub("@\\w+", "", some_txt)
some_txt = gsub("[[:punct:]]", "", some_txt)
some_txt = gsub("[[:digit:]]", "", some_txt)
some_txt = gsub("http\\w+", "", some_txt)
some_txt = gsub("[ \t]{2,}", "", some_txt)
some_txt = gsub("^\\s+|\\s+$", "", some_txt)
some_txt = gsub("amp", "", some_txt)
# define "tolower error handling" function
try.tolower = function(x)
{
y = NA
try_error = tryCatch(tolower(x), error=function(e) e)
if (!inherits(try_error, "error"))
y = tolower(x)
return(y)
}

some_txt = sapply(some_txt, try.tolower)
some_txt = some_txt[some_txt != ""]
names(some_txt) = NULL
return(some_txt)
}



###########################################################


  
   print("Getting tweets...")
# get some tweets
tweets = searchTwitter(keyword, n, lang="en")
# get text 
tweet_txt = sapply(tweets, function(x) x$getText())

# clean text
tweet_clean = clean.text(tweet_txt)
tweet_num = length(tweet_clean)
# data frame (text, sentiment)
tweet_df = data.frame(text=tweet_clean, sentiment=rep("", tweet_num),stringsAsFactors=FALSE)

print("Getting sentiments...")
 # apply function getSentiment
 sentiment = rep(0, tweet_num)
 for (i in 1:tweet_num)
 {
 tmp = getSentiment(tweet_clean[i], db_key)
  
  tweet_df$sentiment[i] = tmp$sentiment
  
  print(paste(i," of ", tweet_num))
 
 
}
  
# delete rows with no sentiment
tweet_df <- tweet_df[tweet_df$sentiment!="",]


#separate text by sentiment
sents = levels(factor(tweet_df$sentiment))
#emos_label <- emos


# get the labels and percents

labels <-  lapply(sents, function(x) paste(x,format(round((length((tweet_df[tweet_df$sentiment ==x,])$text)/length(tweet_df$sentiment)*100),2),nsmall=2),"%"))



nemo = length(sents)
emo.docs = rep("", nemo)
for (i in 1:nemo)
{
  tmp = tweet_df[tweet_df$sentiment == sents[i],]$text
   
   emo.docs[i] = paste(tmp,collapse=" ")
}



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




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