Text censoring system for filtering malicious content using approximate string matching and bayesian filtering

Abstract

Information obtained nowadays often contains malicious contents. These malicious contents such as profane words have to be censored as they can influence the minds of the young ones and create hate among people. In censoring the profane words, this paper introduces a hybrid text censoring method which is based on Bayesian Filtering and Approximate String Matching techniques. The Bayesian filtering technique is used to detect the malicious contents (profane words) while the Approximate String Matching technique is used to enhance the effectiveness of detecting profane words. In evaluating the performance of the proposed system, the evaluation metrics of Precision, Recall, F-measure and MAE were used. The results show that Bayesian filtering technique can be used to filter profane words.