#Install Packages
# install.packages("tm") # for text mining
# install.packages("wordcloud") # word-cloud generator
# install.packages("RColorBrewer") # color palettes
# install.packages("readr")
# install.packages("plyr")
# install.packages("stringr")
# install.packages("stringi")
# install.packages("magrittr")
# install.packages("dplyr")
# install.packages("plotly")
##Load Require Library
library(tm)
## Warning: package 'tm' was built under R version 3.4.4
## Loading required package: NLP
library(RColorBrewer)
library(wordcloud)
## Warning: package 'wordcloud' was built under R version 3.4.4
library(readr)
library("plyr")
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:NLP':
##
## annotate
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
## The following objects are masked from 'package:plyr':
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## arrange, mutate, rename, summarise
## The following object is masked from 'package:stats':
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## filter
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## layout
#Read the Data
tweetsDS <- readRDS("C:\\Users\\206429159\\Documents\\Rstudio\\Midterm\\zynga.rds")
##this should be - M:/S18/CS695/Midterm/Zynga.RDS the forward slashes not backward
tweets <- tweetsDS$text ###the text here is first column on which we should ideally work.. double click on your zynga dataset you will see the coulmn
# Function to clean tweets
clean.text = function(x)
{
# remove unicode
x = gsub("[^\x20-\x7E]", "",x)
# remove rt
x = gsub("rt", "", x)
# remove at
x = gsub("@\\w+", "", x)
# remove punctuation
x = gsub("[[:punct:]]", "", x)
# remove numbers
x = gsub("[[:digit:]]", "", x)
# remove links http
x = gsub("http\\w+", "", x)
# remove tabs
x = gsub("[ |\t]{2,}", "", x)
# remove blank spaces at the beginning
x = gsub("^ ", "", x)
# remove blank spaces at the end
x = gsub(" $", "", x)
# tolower
x = tolower(x)
return(x)
}
# clean tweets
tweets = clean.text(tweets)
Sentiment Analysis of Zynga Users
pos.words = scan('positive-words.txt', what='character', comment.char=';')
neg.words = scan('negative-words.txt', what='character', comment.char=';')
neg.words = c(neg.words, 'wtf', 'fail')
#Implementing our sentiment scoring algorithm
require(plyr)
require(stringr)
## Loading required package: stringr
require(stringi)
## Loading required package: stringi
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
# we got a vector of sentences. plyr will handle a list
# or a vector as an "l" for us
# we want a simple array of scores back, so we use
# "l" + "a" + "ply" = "laply":
scores = laply(sentences, function(sentence, pos.words, neg.words) {
# clean up sentences with R's regex-driven global substitute, gsub():
sentence = gsub('[[:punct:]]', '', sentence)
sentence = gsub('[[:cntrl:]]', '', sentence)
sentence = gsub('\\d+', '', sentence)
# and convert to lower case:
sentence = tolower(sentence)
# split into words. str_split is in the stringr package
word.list = str_split(sentence, '\\s+')
# sometimes a list() is one level of hierarchy too much
words = unlist(word.list)
# compare our words to the dictionaries of positive & negative terms
pos.matches = match(words, pos.words)
neg.matches = match(words, neg.words)
# match() returns the position of the matched term or NA
# we just want a TRUE/FALSE:
pos.matches = !is.na(pos.matches)
neg.matches = !is.na(neg.matches)
# and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
score = sum(pos.matches) - sum(neg.matches)
return(score)
}, pos.words, neg.words, .progress=.progress )
scores.df = data.frame(score=scores, text=sentences)
return(scores.df)
}
sentiment.scores= score.sentiment(tweets, pos.words, neg.words, .progress='none')
score <- sentiment.scores$score
library(plotly)
## Loading required package: ggplot2
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## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
## The following objects are masked from 'package:plyr':
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## arrange, mutate, rename, summarise
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## filter
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## layout
p <- plot_ly(x = ~score, type = "histogram")
p
Zynga User’s Involvement On Each Weekday
tweetsDS$days <- weekdays(as.POSIXlt(tweetsDS$created))
dfrm <-data.frame(table(tweetsDS[,c("isRetweet","days")]))
tweetsDSDays = reshape(dfrm,direction="wide",timevar="days",idvar="isRetweet")
p <- plot_ly(tweetsDSDays, x = ~isRetweet, y = ~Freq.Monday, type = 'bar', name = 'Monday') %>%
add_trace(y = ~Freq.Tuesday, name = 'Tuesday') %>%
add_trace(y = ~Freq.Wednesday, name = 'Wednesday') %>%
add_trace(y = ~Freq.Thursday, name = 'Thursday') %>%
add_trace(y = ~Freq.Friday, name = 'Friday') %>%
add_trace(y = ~Freq.Saturday, name = 'Saturday') %>%
add_trace(y = ~Freq.Sunday, name = 'Sunday') %>%
layout(yaxis = list(title = 'Count'), barmode = 'group')
p
