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server.R
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server.R
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closeAllConnections()
rm(list = ls())
if (!require("pacman")) install.packages("pacman")
pacman::p_load(shiny,
shinydashboard,
Rfacebook,
data.table,
scales,
ggplot2,
dygraphs,
magrittr,
stringi,
stringr,
RTextTools,
tm,
wordcloud,
RColorBrewer,
LSAfun,
tidyverse,
tidytext,
plotly,
xts,
NLP,
openNLP,
tm,
openNLPmodels.en)
shinyServer(function(input, output){
extract_dates_vec <- reactive({
master_data <- readRDS("SET PATH OF THE ndtvpage.rds")
extract_dates_vec <- master_data$created_time
extract_dates_vec <- gsub("T",
" ",
extract_dates_vec)
extract_dates_vec <- as.POSIXct(extract_dates_vec,
tz="America/Los_Angeles")
extract_dates_vec <- format(extract_dates_vec,
tz="Asia/Kolkata",
usetz = T)
extract_dates_vec <- substr(extract_dates_vec,
start = 1,
stop = 10)
extract_dates_vec <- as.Date(extract_dates_vec,
format = "%Y-%m-%d")
})
output$o_min_extract_dates <- renderText({
min_date <- min(extract_dates_vec(), na.rm = T)
min_date <- format(min_date,
format = "%Y-%m-%d")
})
output$o_max_extract_dates <- renderText({
max_date <- max(extract_dates_vec(), na.rm = T)
max_date <- format(max_date,
format = "%Y-%m-%d")
})
#=====================================================================
master_data <- reactive({
master_data <- readRDS("SET PATH OF THE ndtvpage.rds")
master_data <- data.frame(master_data)
master_data$created_time <- substr(master_data$created_time,
start = 1,
stop = 16)
master_data$created_time <- gsub("T",
" ",
master_data$created_time)
master_data$created_time <- as.POSIXct(master_data$created_time,
tz="America/Los_Angeles")
master_data$created_time <- format(master_data$created_time,
tz="Asia/Kolkata",
usetz = T)
master_data$created_time <- as.POSIXct(master_data$created_time,
format="%Y-%m-%d %H:%M")
created_time2 <- master_data$created_time
master_data <- cbind(master_data,
created_time2)
start_date <- paste(input$date_choose[1],
"00:00:00",
"IST",
sep = " ")
start_date <- as.POSIXct(start_date,
format = "%Y-%m-%d %H:%M:%S")
end_date <- paste(input$date_choose[2],
"23:59:00",
"IST",
sep = " ")
end_date <- as.POSIXct(end_date,
format = "%Y-%m-%d %H:%M:%S")
master_data <- subset(master_data,
created_time2 >= start_date & created_time2 <= end_date)
})
# Subsetting only NDTV posts
ndtv_posts <- reactive({
ndtv_posts <- subset(master_data(),
from_name == "NDTV")
})
output$junkstr1 <- renderPrint({
str(ndtv_posts())
})
#Working with ndtv_posts
#Value boxes
output$o_ndtv_posts_no.posts <- renderValueBox({
valueBox("Total no. of Posts",
length(ndtv_posts()$from_name),
icon = icon("clipboard", lib = "font-awesome"))
})
output$o_ndtv_posts_max.likes <- renderValueBox({
valueBox("Maximum no. of Likes on a post",
max(ndtv_posts()$likes_count, na.rm = T),
icon = icon("thumbs-up", lib = "font-awesome"))
})
output$o_ndtv_posts_max.comments <- renderValueBox({
valueBox("Maximum no. of Comments on a post",
max(ndtv_posts()$comments_count, na.rm = T),
icon = icon("comments", lib = "font-awesome"))
})
output$o_ndtv_posts_max.shares <- renderValueBox({
valueBox("Maximum no. of Shares on a post",
max(ndtv_posts()$shares_count, na.rm = T),
icon = icon("share-alt", lib = "font-awesome"))
})
output$o_ndtv_posts_max.loves <- renderValueBox({
valueBox("Maximum no. of Loves on a post",
max(ndtv_posts()$love_count, na.rm = T),
icon = icon("heart", lib = "font-awesome"))
})
output$o_ndtv_posts_max.sad <- renderValueBox({
valueBox("Maximum no. of Sad on a post",
max(ndtv_posts()$sad_count, na.rm = T),
icon = icon("frown", lib = "font-awesome"))
})
#type = media type
output$o_prop_type <- renderPlot({
ggplot(ndtv_posts(), aes(type, fill = type))+
geom_bar(aes(y=..count../sum(..count..)))+
scale_y_continuous(labels=percent_format())+
xlab("Types of media shared") +
ylab("Proportional frequency") +
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold",
colour = "#000000"),
plot.title = element_text(size = 16,
face = "bold"),
panel.background = element_rect(fill= "#FFFFFF"),
panel.border = element_rect(colour = "black",
fill=NA,
size=0.5))
})
output$o_type_likes <- renderPlot({
ggplot(ndtv_posts(),
aes(x = type, y = log(likes_count), fill = type)) +
xlab("Types of media shared") +
ylab("log(Likes)") +
geom_boxplot()+
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold"),
panel.border = element_rect(colour = "black",
fill=NA,
size=0.5))
})
output$o_type_comments <- renderPlot({
ggplot(ndtv_posts(),
aes(x = type, y = log(comments_count), fill = type)) +
xlab("Types of media shared") +
ylab("log(Comments)") +
geom_boxplot()+
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold"),
panel.border = element_rect(colour = "black",
fill=NA,
size=0.5))
})
output$o_type_shares <- renderPlot({
ggplot(ndtv_posts(),
aes(x = type, y = log(shares_count), fill = type)) +
xlab("Types of media shared") +
ylab("log(Shares)") +
geom_boxplot()+
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold"),
panel.border = element_rect(colour = "black",
fill=NA,
size=0.5))
})
# Working with messages that generate the likes, shares and sad
output$o_ndtv_posts_max_l.msg <- renderText({
ndtv_posts2 <- subset(ndtv_posts(),
select = c(likes_count,
message))
ndtv_posts2 <- ndtv_posts2[complete.cases(ndtv_posts2),]
max_l <- which(ndtv_posts2$likes_count == max(ndtv_posts2$likes_count))
ndtv_posts2$message[max_l]
})
output$o_ndtv_posts_max_c.msg <- renderText({
ndtv_posts3 <- subset(ndtv_posts(),
select = c(comments_count,
message))
ndtv_posts3 <- ndtv_posts3[complete.cases(ndtv_posts3),]
max_c <- which(ndtv_posts3$comments_count == max(ndtv_posts3$comments_count))
ndtv_posts3$message[max_c]
})
output$o_ndtv_posts_max_s.msg <- renderText({
ndtv_posts4 <- subset(ndtv_posts(),
select = c(shares_count,
message))
ndtv_posts4 <- ndtv_posts4[complete.cases(ndtv_posts4),]
max_s <- which(ndtv_posts4$shares_count == max(ndtv_posts4$shares_count))
ndtv_posts4$message[max_s]
})
output$o_ndtv_posts_max_lo.msg <- renderText({
ndtv_posts5 <- subset(ndtv_posts(),
select = c(love_count,
message))
ndtv_posts5 <- ndtv_posts5[complete.cases(ndtv_posts5),]
max_lo <- which(ndtv_posts5$love_count == max(ndtv_posts5$love_count))
ndtv_posts5$message[max_lo]
})
output$o_ndtv_posts_max_sa.msg <- renderText({
ndtv_posts6 <- subset(ndtv_posts(),
select = c(sad_count,
message))
ndtv_posts6 <- ndtv_posts6[complete.cases(ndtv_posts6),]
max_sa <- which(ndtv_posts6$sad_count == max(ndtv_posts6$sad_count))
ndtv_posts6$message[max_sa]
})
#Time series graphs
output$o_ndtv_posts_ts_likes <- renderDygraph({
df1 <- data.frame(ndtv_posts()$created_time,
ndtv_posts()$likes_count)
df2 <- as.xts(df1,
order.by = df1[,1])
dygraph(df2,
main = "Number of Likes accross Dates",
xlab = "Dates",
ylab = "No. of Likes") %>%
dyRangeSelector() %>%
dyAxis("y", drawGrid = FALSE)%>%
dyOptions(colors = "red",
drawPoints = T,
pointSize = 3)
})
output$o_ndtv_posts_ts_comments <- renderDygraph({
df3 <- data.frame(ndtv_posts()$created_time,
ndtv_posts()$comments_count)
df4 <- as.xts(df3,
order.by = df3[,1])
dygraph(df4,
main = "Number of Comments accross Dates",
xlab = "Dates",
ylab = "No. of Comments") %>%
dyRangeSelector() %>%
dyAxis("y", drawGrid = FALSE)%>%
dyOptions(colors = "blue",
drawPoints = T,
pointSize = 3)
})
output$o_ndtv_posts_ts_shares <- renderDygraph({
df5 <- data.frame(ndtv_posts()$created_time,
ndtv_posts()$shares_count)
df6 <- as.xts(df5,
order.by = df5[,1])
dygraph(df6,
main = "Number of Shares accross Dates",
xlab = "Dates",
ylab = "No. of Shares") %>%
dyRangeSelector() %>%
dyAxis("y", drawGrid = FALSE)%>%
dyOptions(colors = "green",
drawPoints = T,
pointSize = 3)
})
output$o_ndtv_posts_ts_loves <- renderDygraph({
df7 <- data.frame(ndtv_posts()$created_time,
ndtv_posts()$love_count)
df8 <- as.xts(df7,
order.by = df7[,1])
dygraph(df8,
main = "Number of Love accross Dates",
xlab = "Dates",
ylab = "No. of Love") %>%
dyRangeSelector()%>%
dyAxis("y", drawGrid = FALSE)%>%
dyOptions(colors = "magenta",
drawPoints = T,
pointSize = 3)
})
output$o_ndtv_posts_ts_sad <- renderDygraph({
df9 <- data.frame(ndtv_posts()$created_time,
ndtv_posts()$sad_count)
df10 <- as.xts(df9,
order.by = df9[,1])
dygraph(df10,
main = "Number of Sad accross Dates",
xlab = "Dates",
ylab = "No. of Sad") %>%
dyRangeSelector()%>%
dyAxis("y", drawGrid = FALSE)%>%
dyOptions(colors = "black",
drawPoints = T,
pointSize = 3)
})
#Word cloud
output$o_ndtv_posts_wc <- renderPlot({
ndtv_posts_wc <- str_replace_all(ndtv_posts()$message,
"[^[:alnum:]]",
" ")
ndtv_posts_wc <- removeWords(ndtv_posts_wc,
c("Read more here",
"Image",
"Instagram",
"Minister",
"Chief"))
ndtv_posts_wc <- data.frame(ndtv_posts_wc,
ndtv_posts()$likes_count)
ndtv_posts_wc <- ndtv_posts_wc[complete.cases(ndtv_posts_wc),]
colnames(ndtv_posts_wc) <- c("comp.messages",
"likes")
s <- ndtv_posts_wc$comp.messages
s <- as.String(s)
## Need sentence and word token annotations.
sent_token_annotator <- Maxent_Sent_Token_Annotator()
word_token_annotator <- Maxent_Word_Token_Annotator()
a2 <- annotate(s, list(sent_token_annotator, word_token_annotator))
pos_tag_annotator <- Maxent_POS_Tag_Annotator()
pos_tag_annotator
a3 <- annotate(s, pos_tag_annotator, a2)
a3
## Variant with POS tag probabilities as (additional) features.
head(annotate(s, Maxent_POS_Tag_Annotator(probs = TRUE), a2))
## Determine the distribution of POS tags for word tokens.
a3w <- subset(a3, type == "word")
tags <- sapply(a3w$features, `[[`, "POS")
tags
table(tags)
## Extract token/POS pairs (all of them): easy.
a <- sprintf("%s/%s", s[a3w], tags)
a
words <- s[a3w]
b <- data.frame(words, tags)
NNPwords <- subset(b,
tags == "NNP")
top_talking <- sort(table(NNPwords$words),
decreasing = T)
top_talking <- top_talking[1:5]
barplot(prop.table(top_talking)*100,
col = rainbow(length(top_talking)),
ylim = c(0,
max(prop.table(top_talking)*100)*1.5),
ylab = "Proportional frequency",
xlab = "Topics/personalities",
cex.axis = 1.2,
cex.names = 1.5,
cex.lab = 1.8)
})
#Text summary
output$o_ndtv_posts_summary <- renderText({
ndtv_posts_sm <- iconv(ndtv_posts()$message,
"ASCII", "UTF-8", sub="")
ndtv_posts_sm <- as.character(str_replace_all(ndtv_posts_sm,
"[^[:alnum:]]",
" "))
ndtv_posts_sm <- paste(ndtv_posts_sm, collapse = " ")
summary <- genericSummary(ndtv_posts_sm,
k = 5)
})
#Working with Sentiment analysis
sentiment <- reactive({
sent <- data.frame(words = as.character(ndtv_posts()$message),
date = ndtv_posts()$created_time,
likes = ndtv_posts()$likes_count,
comments = ndtv_posts()$comments_count,
shares = ndtv_posts()$shares_count,
types = ndtv_posts()$type,
stringsAsFactors = F)
sent <- sent[complete.cases(sent),]
pos.words <- scan("SET PATH OF THE FILE positive.csv",
what = "character")
pos.words <- as.character(str_replace_all(pos.words,
"[^[:alnum:]]",
" "))
neg.words <- scan("SET PATH OF THE FILE negative.csv",
what = "character")
neg.words <- as.character(str_replace_all(neg.words,
"[^[:alnum:]]",
" "))
posWords <- pos.words
negWords <- neg.words
wordsDF<- data.frame(words = posWords,
value = 1,
stringsAsFactors=F)
wordsDF<- rbind(wordsDF,
data.frame(words = negWords,
value = -1))
wordsDF$lengths<-unlist(lapply(wordsDF$words,
nchar))
wordsDF<-wordsDF[ order(-wordsDF[,3]),]
scoreSentence <- function(sentence){
score<-0
for(x in 1:nrow(wordsDF)){
count<-length(grep(wordsDF[x,1],sentence))
if(count){
score<-score + (count * wordsDF[x,2])
sentence<-sub(wordsDF[x,1],'',sentence)
}
}
score
}
SentimentScore <- unlist(lapply(sent$words,
scoreSentence))
mat <- cbind(sent, SentimentScore)
mat
})
output$o_sentiment_ts_plots <- renderDygraph({
f <- list(family = "Courier New, monospace",
size = 18,
color = "black")
x <- list(title = "Dates",
titlefont = f)
y <- list(title = "Sentiment scores",
titlefont = f)
p <- plot_ly(x = ~sentiment()$date,
y = ~sentiment()$SentimentScore,
mode = "lines",
marker = list(size = 10,
color = c('red'),
line = list(color = "black",
width = 1)))%>%
layout(xaxis = x, yaxis = y)
p
})
output$o_sentiment_boxplots_likes <- renderPlot({
sts <- boxplot.stats(sentiment()$likes)$stats
ggplot(sentiment(),
aes(x = as.factor(SentimentScore),
y = likes,
fill = as.factor(SentimentScore))) +
xlab("Sentiment scores") +
ylab("Likes") +
geom_boxplot()+
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold"))+
scale_colour_discrete(name = "Sentiment")+
coord_cartesian(ylim = c(sts*1.05,sts/1.05))
})
output$o_sentiment_boxplots_comments <- renderPlot({
sts <- boxplot.stats(sentiment()$comments)$stats
ggplot(sentiment(),
aes(x = as.factor(SentimentScore),
y = comments,
fill = as.factor(SentimentScore))) +
xlab("Sentiment scores") +
ylab("Comments") +
geom_boxplot()+
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold"))+
coord_cartesian(ylim = c(sts*1.05,sts/1.05))
})
output$o_sentiment_boxplots_shares <- renderPlot({
sts <- boxplot.stats(sentiment()$shares)$stats
ggplot(sentiment(),
aes(x = as.factor(SentimentScore),
y = shares,
fill = as.factor(SentimentScore))) +
xlab("Sentiment scores") +
ylab("Shares") +
geom_boxplot()+
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16,
face="bold"))+
coord_cartesian(ylim = c(sts*1.05,sts/1.05))
})
})