Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization

As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis of information content in online social media streams (news articles, blogs,tweets etc.) is the need of the hour as it allows business and government bodies to understand public opinion about products and policies. In most of these settings, data points appear as a stream of high dimensional feature vectors. Guided by real-world industrial deployment scenarios, we revisit the problem of online learning of topics from streaming social media content. On one hand, the topics need to be dynamically adapted to the statistics of incoming datapoints, and on the other hand, early detection of rising new trends is important in many applications. We propose an online nonnegative matrix factorization framework to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization fram...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Ankan Saha, Vikas Sindhwani
• Keywords: Data Mining, Deployment Scenarios, Industrial Deployment, Passive Facilitation, WSDM 2012