A Time-Based Collective Factorization For Topic Discovery And Monitoring In News

Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve, emerge and fade, making it more difficult for the journalist - or the press consumer - to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contextualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics).