I am following an example of Bayesian classifiers according to a Lanz book called Machine Learning with R. The case is a spam classifier that works with data from the following link:
http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/
In the code, I have a problem in this part:
sms_train<-DocumentTermMatrix(sms_corpus_train,list(dictionary=sms_dict))
sms_test<-DocumentTermMatrix(sms_corpus_test,list(dictionary=sms_dict))
because he says that I should use the following statement:
sms_dict <- Dictionary(findFreqTerms(sms_dtm_train, 5))
The problem is that the Dictionary () function is deprecated from new versions of tm. What should I do to accomplish what is written in the books:
A dictionary is a data structure that allows you to specify which words should be displayed in a document matrix. Limit our learning and check matrices only for words in the previous dictionary, use the following command
I have done the following:
sms_dict<-findFreqTerms(sms_dtm_train,5)
sms_train<-DocumentTermMatrix(sms_corpus_train,list(dictionary=sms_dict))
sms_test<-DocumentTermMatrix(sms_corpus_test,list(dictionary=sms_dict))
, , . , , . ?
:
sms_raw<-read.csv("sms_spam.csv",stringsAsFactors=FALSE)
install.packages("tm")
library(tm)
sms_corpus<-Corpus(VectorSource(sms_raw$text))
corpus_clean<-tm_map(sms_corpus,content_transformer(tolower))
corpus_clean<-tm_map(corpus_clean,removeNumbers)
corpus_clean<-tm_map(corpus_clean,removeWords,stopwords())
corpus_clean<-tm_map(corpus_clean,stripWhitespace)
sms_dtm<-DocumentTermMatrix(corpus_clean)
sms_raw_train<-sms_raw[1:4169,]
sms_raw_test<-sms_raw[4170:5559,]
sms_dtm_train<-sms_dtm[1:4169,]
sms_dtm_test<-sms_dtm[4170:5559,]
sms_corpus_train<-corpus_clean[1:4169]
sms_corpus_test<-corpus_clean[4170:5559]
sms_dict<-findFreqTerms(sms_dtm_train,5)
sms_train<-DocumentTermMatrix(sms_corpus_train,list(dictionary=sms_dict))
sms_test<-DocumentTermMatrix(sms_corpus_test,list(dictionary=sms_dict))
convert_counts<-function(x){
x<-ifelse(x>0,1,0)
x<-factor(x,levels=c(0,1),labels=c("No","Yes"))
return(x)
}
sms_train<-apply(sms_train,MARGIN=2,convert_counts)
sms_test<-apply(sms_test,MARGIN=2,convert_counts)
library(e1071)
sms_classifier<-naiveBayes(sms_train,sms_raw_train$type)
sms_test_pred<-predict(sms_classifier,sms_test)
install.packages("gmodels")
library(gmodels)
CrossTable(sms_test_pred,sms_raw_test$type,prop.chisq=FALSE,prop.t=FALSE,dnn=c('predicted','actual'))