Hi!
I will show you how to create a simple application in R & Shiny to perform Twitter Sentiment Analysis in real-time. I use RStudio. First, I create a Shiny Project. Then, in the ui.R file, I put this code:
shinyUI(fluidPage(
titlePanel(“Sentiment Analysis”),
textOutput(“currentTime”),
h4(“Tweets:”),
sidebarLayout(
sidebarPanel(
dataTableOutput(‘tweets_table’)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput(“distPlot”),
sidebarPanel(
plotOutput(“positive_wordcloud”)
),
sidebarPanel(
plotOutput(“negative_wordcloud”)
),
sidebarPanel(
plotOutput(“neutral_wordcloud”)
)
)
)
))
Here, I will show a title, the current time, a table with Twitter user name, a bar graph and wordclouds. Now, I will create the server side:
library(shiny)
library(tm)
library(wordcloud)
library(twitteR)
shinyServer(function(input, output, session) {
setup_twitter_oauth(consumer_key = “xxxxxxxxxxxx”, 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-or-expression-to-evaluate”)
for (tweet in tweets_result){
print(paste(tweet$screenName, “:”, tweet$text))
vector_users <- c(vector_users, as.character(tweet$screenName));
if (grepl(“I love it”, tweet$text, ignore.case = TRUE) == TRUE | grepl(“Wonderful”, tweet$text, ignore.case = TRUE) | grepl(“Awesome”, tweet$text, ignore.case = TRUE)){
count_positive = count_positive + 1
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$text, ignore.case = TRUE)) {
count_negative = count_negative + 1
vector_sentiments <- c(vector_sentiments, “Negative”)
negative_text <- c(negative_text, as.character(tweet$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”)) })
}
})
})
})
It’s a really simply code, not complex at all. The purpose of it is just for testing and so you guys can practice R language. If you have questions just let me know.
Thanks!
Diego.
Follow me on Twitter: https://twitter.com/jdiewitter
