[R] Feature space problem regarding text classification using SVM

[Björn Fisseler]

Dear list members,

I'm currently working on text classification of student's essays, trying 
to identify texts that fit to a certain class or not. I use texts from 
one semester (A) for training and texts from another semester (B) for 
testing the classifier. My workflow is like this:

  * read all texts from A, build a DTM(A) with about 1387 terms
  * read all texts from B, build a DTM(B) with about 626 terms
  * train the classifier with DTM(A), using a SVM (package e1071)

Now I want to classify all texts in DTM(B) using the classifyer. But 
when I try to use predict(), I always get the error message: Error in 
eval(expr, envir, enclos) : object 'XY' not found. As I found out, the 
reason for this is that DTM(A) and DTM(B) have a different number of 
terms and consequently not every term used for training the model is 
available in DTM(B).

My question is: how should/do I deal with this? Should I match the terms 
used in DTM(A) and DTM(B), in order to get an identical feature space? 
This could be achieved either reducing the number of terms in DTM(A) or 
adding several empty/NA columns to DTM(B). Or is there another solution 
to my problem?

Kind regards

   Björn



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