In this paper, we propose a new variant of Latent Dirichlet Allocation (LDA): Collective LDA (C-LDA), for multiple corpora modeling. C-LDA combines multiple corpora during learning such that it can transfer knowledge from one corpus to another; meanwhile it keeps a discriminative node which represents the corpus ID to constrain the learned topics in each corpus. Compared with LDA locally applied to the target corpus, C-LDA results in refined topic-word distribution, while compared with applying LDA globally and straightforwardly to the combined corpus, C-LDA keeps each topic only for one corpus. We demonstrate that C-LDA has improved performance with these advantages by experiments on several benchmark document data sets.