19. An apparatus operable to provide at least one sentiment classifier based on a plurality of features, the apparatus comprising: a processor; and a storage device, operatively connected to the processor, and having stored thereon instructions that, when executed by the processor, cause the processor to: extract the plurality of features from training data, wherein the training data comprises a plurality of units, and wherein those instructions that cause the processor to extract the plurality of features are further operative to cause the processor to: label 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; check at least one element of each unit of the training data according to a plurality of dictionaries to provide dictionary features; detect 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 label 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; determine a value for each feature of the plurality of features based on a frequency at which each feature occurs in the training data; label each unit of the training data according to a plurality of sentiment classes to provide labeled training data; and provide 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.
19. An apparatus operable to provide at least one sentiment classifier based on a plurality of features, the apparatus comprising: a processor; and a storage device, operatively connected to the processor, and having stored thereon instructions that, when executed by the processor, cause the processor to: extract the plurality of features from training data, wherein the training data comprises a plurality of units, and wherein those instructions that cause the processor to extract the plurality of features are further operative to cause the processor to: label 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; check at least one element of each unit of the training data according to a plurality of dictionaries to provide dictionary features; detect 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 label 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; determine a value for each feature of the plurality of features based on a frequency at which each feature occurs in the training data; label each unit of the training data according to a plurality of sentiment classes to provide labeled training data; and provide 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. 20. The apparatus of claim 19 , wherein those instructions that cause the processor to extract the plurality of features are further operative to cause the processor to: check each of the PoS features according to the plurality of dictionaries to provide the dictionary features.