Short Text Processing for Analyzing User Portraits: A Dynamic Combination

Abstract

The rich digital footprint left by users on the Internet has led to extensive researches on all aspects of Internet users. Among them, topic modeling is used to analyze text information posted by users on websites to generate user portraits. For dealing with the serious sparsity problems when extracting topics from short texts by traditional text modeling methods such as Latent Dirichlet Allocation (LDA), researchers usually aggregate all the texts published by each user into a pseudo-document. However, such pseudo-documents contain a lot of irrelevant topics, which is not consistent with the documents published by people in reality. To that end, this paper introduces the LDA-RCC model for dynamic text modeling based on the actual text, which is used to analyze the interests of forum users and build user portraits. Specifically, this combined model can effectively process short texts through the iterative combination of text modeling method LDA and robust continuous clustering method (RCC). Meanwhile, this model can automatically extract the number of topics based on the user’s data. In this way, by processing the clustering results, we can obtain the preferences of each user for deep user analysis. A large number of experimental results show that the LDA-RCC model can obtain good results and is superior to both traditional text modeling methods and short text clustering benchmark methods.