The above limitation motivates us to develop a supervised topic model which can jointly model documents and labels.
we propose a Siamese Labeled Topic Model (SLTM) to exploit the information of documents and labels based on the Siamese network (Bromley et al., 1993;Hu et al., 2014;Wang and Zhang, 2017), where weight matrices in SLTM represent conditional distributions.