SMS Spam Classification

Spam and Ham Classification

Importing Data

#install.packages("tm")
library(tm)
sms<-read.csv("D:/Class PDFs/INT234/Notes/R Data/sms_spam.csv")
str(sms)

'data.frame':   5574 obs. of  2 variables:
 $ type: chr  "ham" "ham" "spam" "ham" ...
 $ text: chr  "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..." "Ok lar... Joking wif u oni..." "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question("| __truncated__ "U dun say so early hor... U c already then say..." ...

Data Analysis and Preprocessing

---

sms$type<-factor(sms$type)
table(sms$type)

 ham spam 
4827  747 

Corpus creation

sms_corpus<-VCorpus(VectorSource(sms$text))
sms_corpus

<<VCorpus>>
Metadata:  corpus specific: 0, document level (indexed): 0
Content:  documents: 5574

as.character(sms_corpus[[1]]) # single document

[1] "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."

lapply(sms_corpus[1:10],as.character) # multiple document

$`1`
[1] "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."

$`2`
[1] "Ok lar... Joking wif u oni..."

$`3`
[1] "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's"

$`4`
[1] "U dun say so early hor... U c already then say..."

$`5`
[1] "Nah I don't think he goes to usf, he lives around here though"

$`6`
[1] "FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv"

$`7`
[1] "Even my brother is not like to speak with me. They treat me like aids patent."

$`8`
[1] "As per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune"

$`9`
[1] "WINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only."

$`10`
[1] "Had your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030"

Converting to lowercase

sms_corpus_clean<-tm_map(sms_corpus,content_transformer(tolower))
sms_corpus_clean

<<VCorpus>>
Metadata:  corpus specific: 0, document level (indexed): 0
Content:  documents: 5574

as.character(sms_corpus[[1]])

[1] "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."

as.character(sms_corpus_clean[[1]])

[1] "go until jurong point, crazy.. available only in bugis n great world la e buffet... cine there got amore wat..."

Removing Numbers,Punctuations and whitespaces

sms_corpus_clean<-tm_map(sms_corpus_clean,removeNumbers)
as.character(sms_corpus_clean[[1]])

[1] "go until jurong point, crazy.. available only in bugis n great world la e buffet... cine there got amore wat..."

sms_corpus_clean<-tm_map(sms_corpus_clean,removePunctuation)
as.character(sms_corpus_clean[[1]])

[1] "go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat"

sms_corpus_clean<-tm_map(sms_corpus_clean,stripWhitespace)
as.character(sms_corpus_clean[[1]])

[1] "go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat"

Removing stopwords

sms_corpus_clean<-tm_map(sms_corpus_clean,removeWords,stopwords())
as.character(sms_corpus_clean[[1]])

[1] "go  jurong point crazy available   bugis n great world la e buffet cine  got amore wat"

Stemming

#install.packages("SnowballC")
library(SnowballC)
sms_corpus_clean<-tm_map(sms_corpus_clean,stemDocument)
as.character(sms_corpus_clean[[1]])

[1] "go jurong point crazi avail bugi n great world la e buffet cine got amor wat"

Tokenization

sms_dtm<-DocumentTermMatrix(sms_corpus_clean)
sms_dtm

<<DocumentTermMatrix (documents: 5574, terms: 6942)>>
Non-/sparse entries: 43749/38650959
Sparsity           : 100%
Maximal term length: 40
Weighting          : term frequency (tf)

---

Data Analyzed and Cleaned

---

Splitting data into train and test categories

sms_train<-sms_dtm[1:4169,]
head(sms_train)

<<DocumentTermMatrix (documents: 6, terms: 6942)>>
Non-/sparse entries: 60/41592
Sparsity           : 100%
Maximal term length: 40
Weighting          : term frequency (tf)

sms_test<-sms_dtm[4170:5574,]
sms_train_labels<-sms$type[1:4169]
head(sms_train_labels)

[1] ham  ham  spam ham  ham  spam
Levels: ham spam

nrow(sms)

[1] 5574

sms_test_labels<-sms$type[4170:5574]

Proportion of spam in training and test data

prop.table(table(sms_train_labels))

sms_train_labels
      ham      spam 
0.8647158 0.1352842 

prop.table(table(sms_test_labels))

sms_test_labels
      ham      spam 
0.8697509 0.1302491 

Visualizing Wordcloud

#install.packages("wordcloud")
library(wordcloud)
wordcloud(sms_corpus_clean,min.freq = 50,random.order=TRUE)

Warning in wordcloud(sms_corpus_clean, min.freq = 50, random.order = TRUE) :
  will could not be fit on page. It will not be plotted.

Finding Frequent terms

sms_freq_words<-findFreqTerms(sms_train,5)
str(sms_freq_words)

 chr [1:1173] "£wk" "abiola" "abl" "abt" "accept" "access" "account" "across" ...

sms_dtm_freq_train<-sms_train[,sms_freq_words]
head(sms_dtm_freq_train)

<<DocumentTermMatrix (documents: 6, terms: 1173)>>
Non-/sparse entries: 49/6989
Sparsity           : 99%
Maximal term length: 13
Weighting          : term frequency (tf)

sms_dtm_freq_test<-sms_test[,sms_freq_words]
head(sms_dtm_freq_test)

<<DocumentTermMatrix (documents: 6, terms: 1173)>>
Non-/sparse entries: 57/6981
Sparsity           : 99%
Maximal term length: 13
Weighting          : term frequency (tf)

Converting 1 to ‘Yes’ and 0 to ‘No’

convert_counts<-function(x){
  x<-ifelse(x>0,'Yes','No')
}

sms_train<-apply(sms_dtm_freq_train,MARGIN = 2,convert_counts)
sms_test<-apply(sms_dtm_freq_test,MARGIN = 2,convert_counts)

library(e1071)
sms_classifier<-naiveBayes(sms_train,sms_train_labels)
sms_test_pred<-predict(sms_classifier,sms_test)
library(gmodels)
confusion_matrix<-CrossTable(sms_test_pred,sms_test_labels,chisq=FALSE,prop.t = FALSE,dnn=c('predicted','actual'))

 
   Cell Contents
|-------------------------|
|                       N |
| Chi-square contribution |
|           N / Row Total |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1405 

 
             | actual 
   predicted |       ham |      spam | Row Total | 
-------------|-----------|-----------|-----------|
         ham |      1214 |        21 |      1235 | 
             |    18.210 |   121.599 |           | 
             |     0.983 |     0.017 |     0.879 | 
             |     0.993 |     0.115 |           | 
-------------|-----------|-----------|-----------|
        spam |         8 |       162 |       170 | 
             |   132.290 |   883.382 |           | 
             |     0.047 |     0.953 |     0.121 | 
             |     0.007 |     0.885 |           | 
-------------|-----------|-----------|-----------|
Column Total |      1222 |       183 |      1405 | 
             |     0.870 |     0.130 |           | 
-------------|-----------|-----------|-----------|

 

confusion_matrix_matrix <- confusion_matrix$t
accuracy<-sum(diag(confusion_matrix_matrix))/sum(confusion_matrix_matrix)
cat("Accuracy is",accuracy*100,"%")

Accuracy is 97.93594 %

Accuracy is 97.9%