1. A computer-implemented method for performing contextual classification of objects, comprising: receiving supervised training data transmitted over a network from at least one content reviewer, the supervised training data comprising at least one comment and at least one tag specifying whether the at least one comment is abusive; storing the supervised training data in a database; preprocessing, with at least one processor, the supervised training data to form at least one vectorized object; training a plurality of models by applying a plurality of machine learning algorithms to each vectorized object in parallel; identifying, with at least one processor, an optimal model from the plurality of models; and filtering at least one abusive comment using the identified optimal model.
1. A computer-implemented method for performing contextual classification of objects, comprising: receiving supervised training data transmitted over a network from at least one content reviewer, the supervised training data comprising at least one comment and at least one tag specifying whether the at least one comment is abusive; storing the supervised training data in a database; preprocessing, with at least one processor, the supervised training data to form at least one vectorized object; training a plurality of models by applying a plurality of machine learning algorithms to each vectorized object in parallel; identifying, with at least one processor, an optimal model from the plurality of models; and filtering at least one abusive comment using the identified optimal model. 2. The computer-implemented method of claim 1 , wherein the at least one content reviewer is selected from the group consisting of an editor, an author, and a third-party user.