A user-oriented special topic generation system for digital newspaper

Abstract

With the coming of digital newspaper, user-oriented special topic generation becomes extremely urgent to satisfy the users’ requirements both functionally and emotionally. We propose an applicable automatic special topic generation system for digital newspapers based on users’ interests. Firstly, extract subject heading vector of the topic of interest by filtering out function words, localizing Latent Dirichlet Allocation (LDA) and training the LDA model. Secondly, remove semantically repetitive vector component by constructing a synonymy word map. Lastly, organize and refine the special topic according to the similarity between the candidate news and the topic, and the density of topic-related terms. The experimental results show that the system has both simple operation and high accuracy, and it is stable enough to be applied for user-oriented special topic generation in practical applications.