We extract hidden topic category information to model topic signature.
we exploit the Latent Dirichlet Allocation (LDA) method (Blei et al., 2003), which is a widely used topic modeling technique, to decompose the topic-word matrix TW into hidden topic categories: TW = TH * HW , where TH is a topic-hidden matrix, HW is hiddenword matrix, and h is the manually-chosen parameter to determine the size of hidden topic categories.