Towards the goal of enhancing interpretability of Latent Dirichlet Allocation (LDA) topics, we propose WikiLDA, an enhancement to LDA using Wikipedia concepts. In WikiLDA, initially, for each document in a corpus we "sprinkle" (append) its most relevant Wikipedia concepts. We then use Generalized Pólya Urn (GPU) to incorporate word-word, word-concept, and concept-concept semantic relatedness into the generative process of LDA. As the most probable concepts from inferred topics can be referred on Wikipedia, the topics are likely to become more interpretable and hence more usable in acquiring domain knowledge from humans for various text mining tasks (e.g. eliciting topic labels for text classification). Empirical results show that a projection of documents by WikiLDA in a semantically enriched and coherent topic space leads to improved performance in text classification like tasks, especially in domains where the classes are hard to separate. © 2017 Copyright held by the owner/author(s).