Text Similarity Computing Based on LDA Topic Model and Word Co-occurrence

Abstract

LDA (Latent Dirichlet Allocation) topic model has been widely applied to text clustering owing to its efficient dimension reduction. The prevalent method is to model text set through LDA topic model, to make inference by Gibbs sampling, and to calculate text similarity with JS (Jensen-Shannon) distance. However, JS distance cannot distinguish semantic associations among text topics. For this defect, a new text similarity computing algorithm based on hidden topics model and word co-occurrence analysis is introduced. Tests are carried out to verify the clustering effect of this improved computing algorithm. Results show that this method can effectively improve text similarity computing result and text clustering accuracy.