GOWSeqStream: an integrated sequential embedding and graph-of-words for short text stream clustering

Recently, the proposed non-parametric Bayesian based techniques which aim to model short-length textual documents through the multinomial distribution on the bag-of-words (BOW), aka mixture model-based approach. Although existing model can effectively deal with the topic/concept drift and textual sparsity problems, they are unable to exploit the semantic sequential representation of text as well as the co-occurrence relationships between words. To meet these challenges, we propose a novel approach called as GOWSeqStream. Our proposed model is a joint integration of graph-of-words (GOW) and deep sequential encoding within the Dirichlet Process Mixture Model (DPMM) framework to improve the performance of text clustering task. Extensive experiments in benchmark real-world datasets demonstrate the effectiveness of our proposed GOWSeqStream model in comparing with recent state-of-the-art baselines. Experimental outputs in terms of NMI standard metric demonstrate the outperformances of proposed GOWSeqStream model over the recent well-known text stream clustering baselines, such as MStream, NPMM and OSDM.