1. A method for at least one processing device to provide at least one sentiment classifier based on a plurality of features, the method comprising: extracting, by the at least one processing device, a plurality of features from training data comprising a plurality of units by: labeling at least one element of each unit of the training data according to a plurality of part-of-speech (PoS) tags to provide PoS features; checking at least one element of each unit of the training data according to a plurality of dictionaries to provide dictionary features; detecting a plurality of negation/inversion elements in the training data to provide a plurality of negation/inversion features based on a negation dictionary and an inversion dictionary of the plurality of dictionaries; and labeling at least one element after each of the plurality of negation/inversion features to provide negated/inverted elements features; determining a value for each feature of the plurality of features based on a frequency at which each feature occurs in the training data; labeling each unit of the training data according to a plurality of sentiment classes to provide labeled training data; and providing the at least one sentiment classifier based on the value of each feature of the plurality of features and the labeled training data using a supervised classification technique.