Hierarchical online NMF for detecting and tracking topic hierarchies in a text stream

Abstract Discovering and tracking topics in a text stream has attracted the interests of many researchers. A limitation of most existing methods is that they organize topics in flat structures. Topic hierarchy could reveal the potential relations between topics, which can help to find high quality topics when analyzing the text stream. In this paper, a hierarchical online non-negative matrix factorization method (HONMF) is proposed to generate topic hierarchies from text streams. The proposed method can dynamically adjust the topic hierarchy to adapt to the emerging, evolving, and fading processes of the topics. In the experiment, HONMF is evaluated under a variety of metrics. Compared with the baseline methods, our method can achieve better performance with competitive time efficiency.