In affect recognition, algorithms commonly use supervised classification, where a classifier is built using features extracted from a labeled training set. Affect lexicons are widely used to provide these features. The simplest lexicon feature is an indicator function, in which a feature $f_L$ takes the value 1 if a particular text contains any word from the relevant lexicon $L$.