Novel comment filtering approach based on outlier on streaming data

This is the preliminary work for a project which will be filtering comments made on news and papers automatically. Our database has over 1 million news and comments. Due to the intensity of our data, 30.677 comments made on 15.064 articles on 44 different categories are used as experimental data. Proposed anomaly based method have been obtained fast and high accuracy results without the high storage requirement and high computational complexity with respect to other classification based methods on literature.