Experimental explorations on short text topic mining between LDA and NMF based Schemes

Learning topics from short texts has become a critical and fundamental task for understanding the widely-spread streaming social messages, e.g., tweets, snippets and questions/answers. Up to date, there are two distinctive topic learning schemes: generative probabilistic graphical models and geometrically linear algebra approaches, with LDA and NMF being the representative works, respectively. Since these two methods both could uncover the latent topics hidden in the unstructured short texts, some interesting doubts are coming to our minds that which one is better and why? Are there any other more effective extensions? In order to explore valuable insights between LDA and NMF based learning schemes, we comprehensively conduct a series of experiments into two parts. Specifically, the basic LDA and NMF are compared with different experimental settings on several public short text datasets in the first part which would exhibit that NMF tends to perform better than LDA; in the second part, we propose a novel model called Knowledge-guided Non-negative Matrix Factorization for Better Short Text Topic Mining (abbreviated as KGNMF), which leverages external knowledge as a semantic regulator with low-rank formalizations, yielding up a time-efficient algorithm. Extensive experiments are conducted on three representative corpora with currently typical short text topic models to demonstrate the effectiveness of our proposed KGNMF. Overall, learning with NMF-based schemes is another effective manner in short text topic mining in addition to the popular LDA-based paradigms. (C) 2018 Elsevier B.V. All rights reserved.