Gsub

G substitute in R

library(twitteR)
library(ggplot2)
library(syuzhet)
# FIll the space with your credentials in the twitter developer
appname <- "     "
key <- "     "
secret <- "      "
access<-"        "
access_secret="       "


setup_twitter_oauth(key, secret, access, access_secret)

tweets_tech <- searchTwitter("#BUDGET_2023", n=100,lang = "en")

## data cleaning
tech_tweets <- twListToDF(tweets_tech)

tech_text<- tech_tweets$text

tech_text<- tolower(tech_text)
tech_text <- gsub("rt", "", tech_text)
tech_text <- gsub("@\\w+", "", tech_text)
tech_text <- gsub("[[:punct:]]", "", tech_text)

#getting emotions using in-built function

mysentiment_tech<-get_nrc_sentiment((tech_text))

#calculationg total score for each sentiment

Sentimentscores_tech<-data.frame(colSums(mysentiment_tech[,]))



names(Sentimentscores_tech)<-"Score"
Sentimentscores_tech<-cbind("sentiment"=rownames(Sentimentscores_tech),Sentimentscores_tech)
rownames(Sentimentscores_tech)<-NULL

#*************************************************************************************


ggplot(data=Sentimentscores_tech,aes(x=sentiment,y=Score))+
 geom_bar(aes(fill=sentiment),stat = "identity")+
 theme(legend.position="none")+
 xlab("Sentiments")+ylab("scores")+ggtitle("Budget 2023")


l=get_nrc_sentiment('ugly')
o=get_nrc_sentiment('delay')
p=get_nrc_sentiment('trust')
p1=get_nrc_sentiment('happy')
p2=get_nrc_sentiment('sad')
p2=get_nrc_sentiment('joy')

Gsub

G substitute in R

library(twitteR)
library(ggplot2)
library(syuzhet)
# FIll the space with your credentials in the twitter developer
appname <- "     "
key <- "     "
secret <- "      "
access<-"        "
access_secret="       "


setup_twitter_oauth(key, secret, access, access_secret)

tweets_tech <- searchTwitter("#BUDGET_2023", n=100,lang = "en")

## data cleaning
tech_tweets <- twListToDF(tweets_tech)

tech_text<- tech_tweets$text

tech_text<- tolower(tech_text)
tech_text <- gsub("rt", "", tech_text)
tech_text <- gsub("@\\w+", "", tech_text)
tech_text <- gsub("[[:punct:]]", "", tech_text)

#getting emotions using in-built function

mysentiment_tech<-get_nrc_sentiment((tech_text))

#calculationg total score for each sentiment

Sentimentscores_tech<-data.frame(colSums(mysentiment_tech[,]))



names(Sentimentscores_tech)<-"Score"
Sentimentscores_tech<-cbind("sentiment"=rownames(Sentimentscores_tech),Sentimentscores_tech)
rownames(Sentimentscores_tech)<-NULL

#*************************************************************************************


ggplot(data=Sentimentscores_tech,aes(x=sentiment,y=Score))+
 geom_bar(aes(fill=sentiment),stat = "identity")+
 theme(legend.position="none")+
 xlab("Sentiments")+ylab("scores")+ggtitle("Budget 2023")


l=get_nrc_sentiment('ugly')
o=get_nrc_sentiment('delay')
p=get_nrc_sentiment('trust')
p1=get_nrc_sentiment('happy')
p2=get_nrc_sentiment('sad')
p2=get_nrc_sentiment('joy')

Gsub

G substitute in R

library(twitteR)
library(ggplot2)
library(syuzhet)
# FIll the space with your credentials in the twitter developer
appname <- "     "
key <- "     "
secret <- "      "
access<-"        "
access_secret="       "


setup_twitter_oauth(key, secret, access, access_secret)

tweets_tech <- searchTwitter("#BUDGET_2023", n=100,lang = "en")

## data cleaning
tech_tweets <- twListToDF(tweets_tech)

tech_text<- tech_tweets$text

tech_text<- tolower(tech_text)
tech_text <- gsub("rt", "", tech_text)
tech_text <- gsub("@\\w+", "", tech_text)
tech_text <- gsub("[[:punct:]]", "", tech_text)

#getting emotions using in-built function

mysentiment_tech<-get_nrc_sentiment((tech_text))

#calculationg total score for each sentiment

Sentimentscores_tech<-data.frame(colSums(mysentiment_tech[,]))



names(Sentimentscores_tech)<-"Score"
Sentimentscores_tech<-cbind("sentiment"=rownames(Sentimentscores_tech),Sentimentscores_tech)
rownames(Sentimentscores_tech)<-NULL

#*************************************************************************************


ggplot(data=Sentimentscores_tech,aes(x=sentiment,y=Score))+
 geom_bar(aes(fill=sentiment),stat = "identity")+
 theme(legend.position="none")+
 xlab("Sentiments")+ylab("scores")+ggtitle("Budget 2023")


l=get_nrc_sentiment('ugly')
o=get_nrc_sentiment('delay')
p=get_nrc_sentiment('trust')
p1=get_nrc_sentiment('happy')
p2=get_nrc_sentiment('sad')
p2=get_nrc_sentiment('joy')

Gsub

G substitute in R

library(twitteR)
library(ggplot2)
library(syuzhet)
# FIll the space with your credentials in the twitter developer
appname <- "     "
key <- "     "
secret <- "      "
access<-"        "
access_secret="       "


setup_twitter_oauth(key, secret, access, access_secret)

tweets_tech <- searchTwitter("#BUDGET_2023", n=100,lang = "en")

## data cleaning
tech_tweets <- twListToDF(tweets_tech)

tech_text<- tech_tweets$text

tech_text<- tolower(tech_text)
tech_text <- gsub("rt", "", tech_text)
tech_text <- gsub("@\\w+", "", tech_text)
tech_text <- gsub("[[:punct:]]", "", tech_text)

#getting emotions using in-built function

mysentiment_tech<-get_nrc_sentiment((tech_text))

#calculationg total score for each sentiment

Sentimentscores_tech<-data.frame(colSums(mysentiment_tech[,]))



names(Sentimentscores_tech)<-"Score"
Sentimentscores_tech<-cbind("sentiment"=rownames(Sentimentscores_tech),Sentimentscores_tech)
rownames(Sentimentscores_tech)<-NULL

#*************************************************************************************


ggplot(data=Sentimentscores_tech,aes(x=sentiment,y=Score))+
 geom_bar(aes(fill=sentiment),stat = "identity")+
 theme(legend.position="none")+
 xlab("Sentiments")+ylab("scores")+ggtitle("Budget 2023")


l=get_nrc_sentiment('ugly')
o=get_nrc_sentiment('delay')
p=get_nrc_sentiment('trust')
p1=get_nrc_sentiment('happy')
p2=get_nrc_sentiment('sad')
p2=get_nrc_sentiment('joy')

Tm

Text mining using R

library(twitteR)
library(ggplot2)
library(syuzhet)
library(tm)

# FIll the space with your credentials in the twitter developer
appname <- "     "
key <- "     "
secret <- "      "
access<-"        "
access_secret="       "


setup_twitter_oauth(key, secret, access, access_secret)

tweets_tech <- searchTwitter("BUDGET 2023", n=100,lang = "en")

a <- twListToDF(tweets_tech)

# library(tm)

corpus = iconv(a$text, "latin1", "UTF-8")
corpus<- Corpus(VectorSource(corpus))

# corpus==>Documents/Docs
# VectorSource==>vector
# a$text==> row/records

toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x))
docs=corpus
docs <- tm_map(docs, toSpace, "/")
docs <- tm_map(docs, toSpace, "@")
docs <- tm_map(docs, toSpace, "\\|")
corpus=docs
corpus<- tm_map(corpus,tolower)
corpus<-tm_map(corpus,removePunctuation)# remove puntuations like , .
corpus<- tm_map(corpus,removeNumbers)
cleanset<-tm_map(corpus,removeWords,stopwords('english'))# remove common words
removeURL<- function(x)gsub('http[[:alnum:]]=','',x)
cleanset<-tm_map(cleanset,content_transformer(removeURL))
x=cleanset


tdm2<-TermDocumentMatrix(cleanset)
tdm2 # display information
tdm2<-as.matrix(tdm2)

write.csv(tdm2,"tdm2.csv")

library(syuzhet)

data=read.csv("tdm2.csv")



mysentiment_tech<-get_nrc_sentiment((data$X))

#calculationg total score for each sentiment

Sentimentscores_tech<-data.frame(colSums(mysentiment_tech[,]))



names(Sentimentscores_tech)<-"Score"
Sentimentscores_tech<-cbind("sentiment"=rownames(Sentimentscores_tech),Sentimentscores_tech)
rownames(Sentimentscores_tech)<-NULL

#*************************************************************************************

ggplot(data=Sentimentscores_tech,aes(x=sentiment,y=Score))+
 geom_bar(aes(fill=sentiment),stat = "identity")+
 theme(legend.position="none")+
 xlab("Sentiments")+ylab("scores")+ggtitle("Budget 2023")