Using LDA to detect semantically incoherent documents

Abstract

Detecting the semantic coherence of a document is a challenging task and has several applications such as in text segmentation and categorization. This paper is an attempt to distinguish between a 'semantically coherent' true document and a 'randomly generated' false document through topic detection in the framework of latent Dirichlet analysis. Based on the premise that a true document contains only a few topics and a false document is made up of many topics, it is asserted that the entropy of the topic distribution will be lower for a true document than that for a false document. This hypothesis is tested on several false document sets generated by various methods and is found to be useful for fake content detection applications.