The most common algorithms involve supervised classification: a training set is labeled for the affective meaning to be detected, and a classifier is built using features extracted from the training set. Many possible values can be used for lexicon features. The simplest is just an indicator function, in which the value of a feature $f_L$ takes the value 1 if a particular text has any word from the relevant lexicon $L$.