Systems and methods for performing contextual classification using supervised and unsupervised training
View Patent ↗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.
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.
3. The computer-implemented method of claim 1 , wherein preprocessing 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.
4. The computer-implemented method of claim 1 , wherein the plurality of machine learning algorithms comprises a support vector machine algorithm.
5. The computer-implemented method of claim 1 , wherein:
preprocessing the supervised training data to form at least one vectorized object comprises preprocessing the supervised training data using a plurality of machines operating in parallel.
6. A system for performing contextual classification of objects, comprising:
a database configured to store supervised training data collected 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;
at least one processor configured to:
preprocess 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
filter at least one abusive comment using the identified optimal model.
7. The system of claim 6 , wherein the at least one content reviewer is selected from the group consisting of an editor, an author, and a third-party user.
8. The system of claim 6 , wherein preprocessing 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.
9. The system of claim 6 , wherein the plurality of machine learning algorithms comprises a support vector machine algorithm.
10. The system of claim 6 , wherein:
preprocessing the supervised training data to form at least one vectorized object comprises preprocessing the supervised training data using a plurality of machines operating in parallel.
11. A non-transitory 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 steps comprising:
collecting supervised training data 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;
preprocessing 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
filtering at least one abusive comment using the identified optimal model.
12. The non-transitory computer-readable storage medium of claim 11 , wherein the at least one content reviewer is selected from the group consisting of an editor, an author, and a third-party user.
13. The non-transitory computer-readable storage medium of claim 11 , wherein the step of preprocessing the supervised training data comprises performing at least one operation selected from the group consisting of stemming, tokenization, and n-gram analysis.
14. The non-transitory computer-readable storage medium of claim 11 , wherein the plurality of machine learning algorithms comprises a support vector machine algorithm.
15. The non-transitory computer-readable storage medium of claim 11 , wherein:
the step of preprocessing the supervised training data comprises preprocessing the supervised training data using a plurality of machines operating in parallel.