Modeling emerging, evolving and fading topics using dynamic soft orthogonal NMF with sparse representation

Dynamic topic models (DTM) are of great use toanalyze the evolution of unobserved topics of a text collectionover time. Recent years have witnessed the explosive growth ofstreaming text data emerging from online media, which createsan unprecedented need for DTMs for timely event analysis. While there have been some matrix factorization methods inthe literature for dynamic topic modeling, further study is stillin great need to model emerging, evolving and fading topicsin a more natural and effective way. In light of this, we firstpropose a matrix factorization model called SONMFSR (SoftOrthogonal NMF with Sparse Representation), which makes fulluse of soft orthogonal and sparsity constraints for static topicmodeling. Furthermore, by introducing the constraints of emerging, evolving and fading topics to SONMFSR, we easily obtain a novel DTM called SONMFSRd for dynamic event analysis. Extensive experiments on two public corpora demonstrate the superiority of SONMFSRd to some state-of-the-art DTMs in both topic detection and tracking. In particular, SONMFSRd shows great potential in real-world applications, where popular topics in Two Sessions 2015 are captured and traced dynamically for possible insights.