I want to build a document classifier in R, using the Naive Bayes approach.
Here are steps, that I've done so far:
- I have corpus with about 30 documents from 2 authors (Classes are: "target author" and "other author").
- "Vocabulary" (training set) has been pre-processed (removed numbers, removed punctuation, words to lower case, removed stop words, stem documents, strip whitespace), and I am considering only frequent words (top 700).
Now I have matrix which looks like:
Then I trained my classifier using Bayes using some existing R library, e1071.
Here are my questions:
I want to test my classifier on other documents that were not part of the training set.
- How to prepare my data matrix? What if those other documents don't contain all the words (attributes) from my training set? Should I put dummy columns there (e.g., with
value=0)? - Does the position of the words (columns order) matter?
Here is an example:
Training attributes:
"wild" "wind" "woman"
Testing attributes:
"woman" "wind" "wild"
Is this ok, or should columns be in the same order as in training matrix?
