Understanding text conversion into SVM input
Text can be converted to data via the use of concept clusters (after stemming and stopping), or to count (frequencies) via use of n-grams.  N-grams are basically tabulations of the 1-gram count (frequency) of alphabet characters (a though z) in each document, and counts of 2-grams (aa to zz), 3-grams (aaa through zzz), up to about 5-grams (aaaaa through zzzzz).  Beyond 5-grams, the data will be sparse and less informative.  Thus, a dataset can be constructed for which rows represent documents, and columns represent n-grams. The data values themselves are the total number of occurrences of each gram found in each document.  

FYI - n-grams have proven to be the best technique for identifying different languages based on characters.    

Regarding SVMs, focus on the SVM literature.