Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization

Understanding large-scale document collections in an efficient manner is an important problem. Usually, document data are associated with other information (e.g., an author's gender, age, and location) and their links to other entities (e.g., co-authorship and citation networks). For the analysis of such data, we often have to reveal common as well as discriminative characteristics of documents with respect to their associated information, e.g., male- vs. female-authored documents, old vs. new documents, etc. To address such needs, this paper presents a novel topic modeling method based on joint nonnegative matrix factorization, which simultaneously discovers common as well as discriminative topics given multiple document sets. Our approach is based on a block-coordinate descent framework and is capable of utilizing only the most representative, thus meaningful, keywords in each topic through a novel pseudo-deflation approach. We perform both quantitative and qualitative evaluations using synthetic as well as real-world document data sets such as research paper collections and nonprofit micro-finance data. We show our method has a great potential for providing in-depth analyses by clearly identifying common and discriminative topics among multiple document sets.