A method of filtering bad text in network based on topic sensitive words tagging

Abstract

Abstract Bad information filtering is conducive to creating a healthy network. A two-level filtering method based on topic and sensitive words is proposed. In the first stage, the network text is filtered by using thesaurus, by setting the weight of different topics. In the second stage, according to the frequency, position and sensitivity of sensitive words, the value of bad tendency is obtained by weighting the web text. Finally, taking the text set recognition of bad financial publicity content in the network as an example, the result proves that it can improve the efficiency and accuracy of filtering of bad investment information.