The Discovery of Burst Topic and Its Intermittent Evolution in Our Real World

• Published in: China communications Vol. 10; no. 3; pp. 1 - 12
• Main Authors: Tang, Siliang, Zhang, Yin, Wang, Hanqi, Chen, Ming, Wu, Fei, Zhuang, Yueting
• Format: Journal Article
• Language: English
• Published: China Communications Magazine Co. Ltd 01.03.2013
• Subjects: Cluster approximation; Context modeling; Data models; iterative clustering model; Iterative methods; LDA; Predictive models; Resource management; time series; 新闻网站; 演变; 狄利克雷; 现实世界; 跟踪检测; 间歇; 预测能力
• ISSN: 1673-5447
• DOI: 10.1109/CC.2013.6488826

Nowadays, a considerably large number of documents are available over many online news sites （e.g., CNN and NYT）. Therefore, the utilization of these online documents, for example, the discovery of a burst topic and its evolution, is a significant challenge. In this paper, a novel topic model, called intermittent Evolution LDA （iELDA） is proposed. In iELDA, the time-evolving docu- ments are divided into many small epochs. iELDA utilizes the detected global topics as priors to guide the detection of an emerging topic and keep track of its evolution over dif- ferent epochs. As a natural extension of the traditional Latent Dirichlet Allocation （LDA） and Dynamic Topic Model （DTM）, iELDA has an advantage： it can discover the intermit- tent recurring pattern of a burst topic. We apply iELDA to real-world data from NYT; the results demonstrate that the proposed iELDA can appropriately capture a burst topic and track its intermittent evolution as well as produce a better predictive ability than other related topic models.