Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-21. (canceled) 22. A computer-implemented method for performing contextual classification of objects, the method comprising the following operations performed by one or more processors: receiving supervised training data transmitted over a network from at least one content reviewer; processing 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 an optimal model from the plurality of models; and classifying a candidate object using the identified optimal model. 23. The computer-implemented method of claim 22, wherein the at least one content reviewer is selected from the group consisting of an editor, an author, and a third-party user. 24. The computer-implemented method of claim 22, wherein processing the supervised training data to form at least one vectorized object comprises performing at least one operation selected from the group consisting of stemming, tokenization, and n-gram analysis. 25. The computer-implemented method of claim 22, wherein the plurality of machine learning algorithms comprises a support vector machine algorithm. 26. The computer-implemented method of claim 22, wherein processing the supervised training data to form at least one vectorized object comprises processing the supervised training data using a plurality of machines operating in parallel. 27. The computer-implemented method of claim 22, wherein: receiving supervised training data transmitted over a network from at least one content reviewer comprises receiving at least one pre-defined tag selected by the at least one content reviewer as being associated with at least one training article; and classifying a candidate object using the identified optimal model comprises automatically classifying a candidate article. 28. The computer-implemented method of claim 22, wherein: receiving supervised training data transmitted over a network from at least one content reviewer comprises receiving at least one tag selected by a content reviewer specifying whether at least one comment associated with an article is abusive; and classifying a candidate object using the identified optimal model comprises automatically filtering at least one abusive comment. 29. A system for performing contextual classification of objects, comprising: a memory configured to store supervised training data collected from at least one content reviewer; at least one processor configured to: process the supervised training data to form at least one vectorized object; train a plurality of models by applying a plurality of machine learning algorithms to each vectorized object in parallel; identify an optimal model from the plurality of models; and classify a candidate object using the identified optimal model. 30. The system of claim 29, wherein the at least one content reviewer is selected from the group consisting of an editor, an author, and a third-party user. 31. The system of claim 29, wherein to process the supervised training data to form at least one vectorized object the at least processor is configured to perform at least one operation selected from the group consisting of stemming, tokenization, and n-gram analysis. 32. The system of claim 29, wherein the plurality of machine learning algorithms comprises a support vector machine algorithm. 33. The system of claim 29, wherein to process the supervised training data to form at least one vectorized object the at least processor is configured to process the supervised training data using a plurality of machines operating in parallel. 34. The system of claim 29, wherein: the memory is further configured to store at least one pre-defined tag selected by the at least one content reviewer in association with at least one training article; and the at least one processor is further configured to classify a candidate object using the identified optimal model by automatically classifying a candidate article. 35. The system of claim 29, wherein: the memory is further configured to store at least one tag selected by a content reviewer specifying whether at least one comment associated with an article is abusive; and the at least one processor is further configured to classify a candidate object using the identified optimal model by automatically filtering at least one abusive comment. 36. A computer-readable storage medium including instructions for performing contextual classification of objects, which, when executed by at least one processor, cause the processor to perform operations comprising: collecting supervised training data from at least one content reviewer; processing 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 an optimal model from the plurality of models; and classifying a candidate object using the identified optimal model. 37. The computer-readable storage medium of claim 36, wherein the at least one content reviewer is selected from the group consisting of an editor, an author, and a third-party user. 38. The computer-readable storage medium of claim 36, wherein the operations performed by the at least one processor further comprise performing at least one operation selected from the group consisting of stemming, tokenization, and n-gram analysis. 39. The computer-readable storage medium of claim 36, wherein the plurality of machine learning algorithms comprises a support vector machine algorithm. 40. The computer-readable storage medium of claim 36, wherein the operations performed by the at least one processor further comprise processing the supervised training data using a plurality of machines operating in parallel. 41. The computer-readable storage medium of claim 36, wherein the operations performed by the at least one processor further comprise: collecting at least one pre-defined tag selected by the at least one content reviewer as being associated with at least one training article; and automatically classifying a candidate article. 42. The computer-readable storage medium of claim 36, wherein the operations performed by the at least one processor further comprise: collecting at least one tag selected by a content reviewer specifying whether at least one comment associated with an article is abusive; and automatically filtering at least one abusive comment.