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shinyUI(fluidPage( 
titlePanel("Sentiment Analysis"), #Title
textOutput("currentTime"),   #Here, I show a real time clock
h4("Tweets:"),   #Sidebar title
sidebarLayout(
sidebarPanel(
dataTableOutput('tweets_table') #Here I show the users and the sentiment
),
mainPanel(
plotOutput("distPlot"), #Here I will show the bars graph
sidebarPanel(
plotOutput("positive_wordcloud") #Cloud for positive words
),
sidebarPanel(
plotOutput("negative_wordcloud") #Cloud for negative words
),
sidebarPanel(
plotOutput("neutral_wordcloud") #Cloud for neutral words
)))))

library(shiny)
library(tm)
library(wordcloud)
library(twitteR)
 
shinyServer(function(input, output, session) {
  setup_twitter_oauth(consumer_key = "xxxxxxxxxx", consumer_secret = "xxxxxxxxxxxx")
  token <- get("oauth_token", twitteR:::oauth_cache)
  token$cache()
  output$currentTime <- renderText({invalidateLater(1000, session)
                                  paste("Current time is: ",Sys.time())})
   observe({
     invalidateLater(60000,session)
    count_positive = 0
    count_negative = 0
    count_neutral = 0
    positive_text <- vector()
    negative_text <- vector()
    neutral_text <- vector()
    vector_users <- vector()
    vector_sentiments <- vector()
    tweets_result = ""
    tweets_result = searchTwitter("word-to-find-in-twitter")
    for (tweet in tweets_result){
      print(paste(tweet$screenName, ":", tweet$text))
       vector_users <- c(vector_users, as.character(tweet$screenName));
       if (grepl("lindo", tweet$text, ignore.case = TRUE) == TRUE | grepl("Wonderful", tweet$text, ignore.case = TRUE) | grepl("Awesome", tweet$text, ignore.case = TRUE)){
        count_positive = count_positive + 1
        print("positivo")
         vector_sentiments <- c(vector_sentiments, "Positive")
        positive_text <- c(positive_text, as.character(tweet$text))
       } else if (grepl("Boring", tweet$text, ignore.case = TRUE) | grepl("I'm sleeping", tweet$text, ignore.case = TRUE)) { 
        count_negative = count_negative + 1
        print("negativo")
        vector_sentiments <- c(vector_sentiments, "Negative")
        negative_text <- c(negative_text, as.character(tweet$text))
       } else {
        count_neutral = count_neutral + 1
        print("neutral")
        vector_sentiments <- c(vector_sentiments, "Neutral")
        neutral_text <- c(neutral_text, as.character(neutral_text))
      }
    }
    df_users_sentiment <- data.frame(vector_users, vector_sentiments)
    output$tweets_table = renderDataTable({
      df_users_sentiment
    })

    output$distPlot <- renderPlot({
      results = data.frame(tweets = c("Positive", "Negative", "Neutral"), numbers = c(count_positive,count_negative,count_neutral))
      barplot(results$numbers, names = results$tweets, xlab = "Sentiment", ylab = "Counts", col = c("Green","Red","Blue"))
      if (length(positive_text) > 0){
        output$positive_wordcloud <- renderPlot({ wordcloud(paste(positive_text, collapse=" "), min.freq = 0, random.color=TRUE, max.words=100 ,colors=brewer.pal(8, "Dark2"))  })
      }
      if (length(negative_text) > 0) {
        output$negative_wordcloud <- renderPlot({ wordcloud(paste(negative_text, collapse=" "), random.color=TRUE,  min.freq = 0, max.words=100 ,colors=brewer.pal(8,"Set3"))  })
      }
      if (length(neutral_text) > 0){
        output$neutral_wordcloud <- renderPlot({ wordcloud(paste(neutral_text, collapse=" "), min.freq = 0, random.color=TRUE , max.words=100 ,colors=brewer.pal(8, "Dark2"))  })
      }
    })
  })
})