Shiny App Demo

server.R

library(tm)
library(twitteR)
library(stringr)
library(plyr)

#get the tweets
#tweets = local(get(load('D:/SureshPersonal/Coursera/SentiAnalysis/Twitter/CE.RData')))
tweets = local(get(load('C:/shiny/sat.RData')))
tweets_txt = sapply(tweets,function(x) x$getText())

#function to clean data
cleanTweets = function(tweets)
{
tweets_cl = gsub("(RT|via)((?:\\b\\W*@\\w+)+)","",tweets)
tweets_cl = gsub("http[^[:blank:]]+", "", tweets_cl)
tweets_cl = gsub("@\\w+", "", tweets_cl)
tweets_cl = gsub("[ \t]{2,}", "", tweets_cl)
tweets_cl = gsub("^\\s+|\\s+$", "", tweets_cl)
tweets_cl = gsub("[[:punct:]]", " ", tweets_cl)
tweets_cl = gsub("[^[:alnum:]]", " ", tweets_cl)
tweets_cl <- gsub('\\d+', '', tweets_cl)
return(tweets_cl)
}

#function to calculate number of words in each category within a sentence
sentimentScore <- function(sentences, vNegTerms, negTerms, posTerms, vPosTerms){
  final_scores <- matrix('', 0, 5)
  scores <- laply(sentences, function(sentence, vNegTerms, negTerms, posTerms, vPosTerms){
    initial_sentence <- sentence
    #remove unnecessary characters and split up by word 
  sentence = cleanTweets(sentence)	
    sentence <- tolower(sentence)	
	wordList <- str_split(sentence, '\\s+')
    words <- unlist(wordList)
    #build vector with matches between sentence and each category
    vPosMatches <- match(words, vPosTerms)
    posMatches <- match(words, posTerms)
    vNegMatches <- match(words, vNegTerms)
    negMatches <- match(words, negTerms)
    #sum up number of words in each category
    vPosMatches <- sum(!is.na(vPosMatches))
    posMatches <- sum(!is.na(posMatches))
    vNegMatches <- sum(!is.na(vNegMatches))
    negMatches <- sum(!is.na(negMatches))
    score <- c(vNegMatches, negMatches, posMatches, vPosMatches)
    #add row to scores table
    newrow <- c(initial_sentence, score)
    final_scores <- rbind(final_scores, newrow)
    return(final_scores)
  }, vNegTerms, negTerms, posTerms, vPosTerms)
  return(scores)
}


#load pos,neg statements
afinn_list <- read.delim(file='C:/shiny/AFINN/AFINN-111.txt', header=FALSE, stringsAsFactors=FALSE)
names(afinn_list) <- c('word', 'score')
afinn_list$word <- tolower(afinn_list$word) 

#categorize words as very negative to very positive and add some movie-specific words
vNegTerms <- afinn_list$word[afinn_list$score==-5 | afinn_list$score==-4]
negTerms <- c(afinn_list$word[afinn_list$score==-3 | afinn_list$score==-2 | afinn_list$score==-1], "second-rate", "moronic", "third-rate", "flawed", "juvenile", "boring", "distasteful", "ordinary", "disgusting", "senseless", "static", "brutal", "confused", "disappointing", "bloody", "silly", "tired", "predictable", "stupid", "uninteresting", "trite", "uneven", "outdated", "dreadful", "bland")
posTerms <- c(afinn_list$word[afinn_list$score==3 | afinn_list$score==2 | afinn_list$score==1], "first-rate", "insightful", "clever", "charming", "comical", "charismatic", "enjoyable", "absorbing", "sensitive", "intriguing", "powerful", "pleasant", "surprising", "thought-provoking", "imaginative", "unpretentious")
vPosTerms <- c(afinn_list$word[afinn_list$score==5 | afinn_list$score==4], "uproarious", "riveting", "fascinating", "dazzling", "legendary")    
 
#Calculate score on each tweet
tweetResult <- as.data.frame(sentimentScore(tweets_txt, vNegTerms, negTerms, posTerms, vPosTerms))
 tweetResult$'2' = as.numeric(tweetResult$'2')
tweetResult$'3' = as.numeric(tweetResult$'3')
tweetResult$'4' = as.numeric(tweetResult$'4')
tweetResult$'5' = as.numeric(tweetResult$'5')
 Score_Neg = sum(tweetResult$'3',tweetResult$'2')
 Score_Pos = sum(tweetResult$'4',tweetResult$'5')
global_score = round( 100 * Score_Pos / (Score_Pos + Score_Neg) )
counts = c(sum(tweetResult$'2'),sum(tweetResult$'3'),sum(tweetResult$'4'),sum(tweetResult$'5'))
names = c("Worst","BAD","GOOD","VERY GOOD")
mr = list(counts,names)
colors = c("red", "yellow", "green", "violet")

# Define server logic required to plot various variables against mpg
shinyServer(function(input, output) {  
  output$barplot <- renderPlot({
    barplot(mr[[1]], main="Movie Review", xlab="Number of votes",legend=mr[[2]],col=colors)
  })
})

ui.R

library(shiny)

# Define UI for miles per gallon application
shinyUI(pageWithSidebar(

  # Application title
  headerPanel("Chennai Express"),
sidebarPanel(),
  mainPanel( 
    plotOutput("barplot")
  )
))