WhatsappAnalayzer.R

#Load required packages
library(ggplot2)
library(lubridate)
library(Scale)
library(reshape2)
library(tm)
library(SnowballC)
library(wordcloud)
library(RColorBrewer)
library(stringr)
library(syuzhet) 
library(dplyr ) 
library(textclean)

#get the data from whatsapp chat 
text <- readLines("chat.txt")

#remove ascii
Encoding(text) <- "latin1"
text <- replace_non_ascii(text)

#remove warning for tm_map
doc_id <- c(1)
df <- data.frame(doc_id = doc_id, text = text, stringsAsFactors = FALSE)
docs <- Corpus(DataframeSource(df))

#clean our chat data
trans <- content_transformer(function (x , pattern ) gsub(pattern, "", x))
docs <- tm_map(docs, trans, "/")
docs <- tm_map(docs, trans, "@")
docs <- tm_map(docs, trans, "\\|")
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeNumbers)
docs <- tm_map(docs, removeWords, stopwords("english"))
docs <- tm_map(docs, removeWords, c("sudharsan","friendName"))
docs <- tm_map(docs, removePunctuation)
docs <- tm_map(docs, stripWhitespace)
docs <- tm_map(docs, stemDocument)

#create the document term matrix
dtm <- TermDocumentMatrix(docs)
mat <- as.matrix(dtm)
v <- sort(rowSums(mat),decreasing=TRUE)

#Data frame
data <- data.frame(word = names(v),freq=v)
head(data, 10)


#generate the wordcloud
set.seed(1056)
wordcloud(words = data$word, freq = data$freq, min.freq = 1,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))


#fetch sentiment words from texts
Sentiment <- get_nrc_sentiment(text)
head(Sentiment)
text <- cbind(text,Sentiment)

#count the sentiment words by category
TotalSentiment <- data.frame(colSums(text[,c(2:11)]))
names(TotalSentiment) <- "count"
TotalSentiment <- cbind("sentiment" = rownames(TotalSentiment), TotalSentiment)
rownames(TotalSentiment) <- NULL

#total sentiment score of all texts
ggplot(data = TotalSentiment, aes(x = sentiment, y = count)) +
geom_bar(aes(fill = sentiment), stat = "identity") +
theme(legend.position = "none") +
xlab("Sentiment") + ylab("Total Count") + ggtitle("Total Sentiment Score")