Discovering author interest evolution in order-sensitive and Semantic-aware topic modeling

Modeling the interests of authors over time from documents has important applications in broad applications such as recommendation systems, authorship identification and opinion extraction. In this paper, we propose an Ordering-sensitive and Semantic-aware Dynamic Author Topic Model (OSDATM), which monitors the evolution of author interest in time-stamped documents. The model further uses the discovered author interest information to discover better topics. Unlike traditional topic models, OSDATM is sensitive to the ordering of words, thus it extracts more information from the semantic meaning of the context. The experimental results show that OSDATM learns better topics than state-of-the-art topic models. In addition, the dynamic interests of authors that the OSDATM model discovers are interpretable and consistent with the truth. © 2019