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 i...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 documents 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 different 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 intermittent 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.展开更多
基金supported by the National Basic Research Program of China under Grant No. 2012CB316400the National High Technology Research and Development Program of China under Grant No. 2012AA012505the Fundamental Research Funds for the Central Universities
文摘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 documents 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 different 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 intermittent 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.
