Classifying Email in R
( All emails are or in french or in english )

Pre-processing methods :


Merge " Summary " and " Content " which are the head of the email and the content
Remove all emails adress
Remove all " From : Someone To : someone ... subject : something "
Remove all images included in the email
Order the email according to their class
Replace all french accent by no accent like é -> e ; ê -> e ...
Put text to lower
remove punctuation
remove numbers
strip white space
remove some first name from a list and some specific words
remove stop words in french and english
stem document in french and english
Remove term 


--> Then Document term matrix, with TF-IDF

The two classes are from a technical support, two categories "complex" and "easy", "complex" are topics around finance ( in theory ) , "simple" problem with the software ( in theory ) but in practice they have a lot words in common.
And the other classes I don't take them into account I just focus on the two for now

Actually the kind of classification algorithm I used is not so relevant because I tried with 5 algo and none gave good results 

Example ( confusion matrix )
Decision tree: 

  Decision tree:           


pred: 

335 | 10
59  | 12            

SVM:


331  | 1
83   | 1                     

Knn (n=10):


330  | 2

83   | 1 

Naive Bayes:


1   |  83

12   |  320