We treat the task of sentiment analysis as a supervised learning problem, where we are given labeled data {(x i , y i )} n i=1 and the goal is to estimate a decision function f (x) → y that maps input examples to labels.
we use a linear SVM model with the prediction function of the following form: f = sign(w T x + b), where the model weights w are estimated from the training set.