Modeling Both Coarse-Grained and Fine-Grained Topics in Massive Text Data
Weifan Zhang, Hui Zhang, Yuan Zuo, Deqing Wang · 2015
Topic model has attracted much attention from investigators, as it provides users with insights into the huge volumes of documents. However, most previous related studies that based on Non-negative Matrix Factorization (NMF) neglect to figure out which topics are widespread in the documents and which are not. These widespread topics, which we refer to coarse-grained topics, have great significance for people who concentrate on common topics in a given text set. For example, after reading the massive job ads, the jobseekers are eager to learn employers' basic requirements which can be regarded as the coarse-grained topics, as well as the additional requirements that can be deemed to be the fine-grained topics. In this paper, we propose a novel method which applies two different sparseness constraints to NMF to tell coarse-grained topics and fine-grained topics apart. The experimental results of demonstrate that the new model can not only discover coarse-grained topics but also extract fine-grained topics. We evaluate the performance of the new model via text clustering and classification, and the results show the new model can learn more accurate topic representations of documents.