JACIII Vol.20 No.1 pp. 66-75
doi: 10.20965/jaciii.2016.p0066


Topic Evolution Analysis Based on Cluster Topic Model

Yaoyi Xi, Gang Chen, Bicheng Li, and Yongwang Tang

Zhengzhou Information Science and Technology Institute
Zhengzhou 450001, China

May 7, 2015
November 18, 2015
Online released:
January 19, 2016
January 20, 2016
topic evolution, cluster topic model, topic evolution graph, Dirichlet process, event detection

Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two events is identified based on the aspect distributions of documents. Finally, by choosing the representative document of each event, topic evolution graph is constructed to display the development of the topic along the timeline. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.

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Last updated on Mar. 28, 2017