IMS-DTM: Incremental Multi-Scale Dynamic Topic Models

Authors

  • Xilun Chen Arizona State University
  • K. Selcuk Candan Arizona State University
  • Maria Luisa Sapino University of Torino

Keywords:

Incremental Analysis, Dynamic Topic Models, Gibbs Sampling, Multi-Scale

Abstract

Dynamic topic models (DTM) are commonly used for mining latent topics in evolving web corpora. In this paper, we note that a major limitation of the conventional DTM based models is that they assume a predetermined and fixed scale of topics. In reality, however, topics may have varying spans and topics of multiple scales can co-exist in a single web or social media data stream. Therefore, DTMs that assume a fixed epoch length may not be able to effectively capture latent topics and thus negatively affect accuracy. In this paper, we propose a Multi-Scale Dynamic Topic Model (MS-DTM) and a complementary Incremental Multi-Scale Dynamic Topic Model (IMS-DTM) inference method that can be used to capture latent topics and their dynamics simultaneously, at different scales. In this model, topic specific feature distributions are generated based on a multi-scale feature distribution of the previous epochs; moreover, multiple scales of the current epoch are analyzed together through a novel multi-scale incremental Gibbs sampling technique. We show that the proposed model significantly improves efficiency and effectiveness compared to the single scale dynamic DTMs and prior models that consider only multiple scales of the past.


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Published

2018-04-27

How to Cite

Chen, X., Candan, K. S., & Sapino, M. L. (2018). IMS-DTM: Incremental Multi-Scale Dynamic Topic Models. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11988