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; labeling at least one element after each of the plurality of negation/inversion features to provide negated/inverted elements features based on at least one of a propagation window or punctuation in the training data; determining, by the at least one processing device, a value for each feature of the plurality of features based on a frequency at which each feature occurs in the training data; labeling, by the at least one processing device, each unit of the training data according to a plurality of sentiment classes to provide labeled training data; and providing, by the at least one processing device, 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.