In Support Vector Machines, when used for sentiment analysis, text gets converted into a set of data points. How does this happen, usually?
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.
Well the text doesn't get converted into data points ... Let's say we are doing sentence level opinion mining.. Features are extracted from a sentence . Now it depends on case to case as to what features to use... A common one is bag of words models in which features become distinct words in sentence and the value of features are the frequency it is repeated in a sentence. Those frequencies are your data points.