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Topic evolution based on the probabilistic topic model: a review

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Abstract

Accurately representing the quantity and characteristics of users’ interest in certain topics is an important problem facing topic evolution researchers, particularly as it applies to modern online environments. Search engines can provide information retrieval for a specified topic from archived data, but fail to reflect changes in interest toward the topic over time in a structured way. This paper reviews notable research on topic evolution based on the probabilistic topic model from multiple aspects over the past decade. First, we introduce notations, terminology, and the basic topic model explored in the survey, then we summarize three categories of topic evolution based on the probabilistic topic model: the discrete time topic evolution model, the continuous time topic evolutionmodel, and the online topic evolution model. Next, we describe applications of the topic evolution model and attempt to summarize model generalization performance evaluation and topic evolution evaluation methods, as well as providing comparative experimental results for different models. To conclude the review, we pose some open questions and discuss possible future research directions.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions, which significantly contributed to improving the manuscript. This work was supported by the National Key Basic Research Project of China (973 Program) (2012CB316400), the National Nature Science Foundation of China (Grant Nos. 61471321, 61202400, 31300539, and 31570629), the Zhejiang Provincial Natural Science Foundation of China (LY15C140005, LY16F010004), Science and Technology Department of Zhejiang Province Public Welfare Project (2016C31G2010057, 2015C31004), Fundamental Research Funds for the Central Universities (172210261) and the Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research.

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Authors and Affiliations

  1. College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310027, China

    Houkui Zhou, Huimin Yu & Roland Hu

  2. State Key Laboratory of CAD & CG, Hangzhou, 310027, China

    Huimin Yu

  3. School of Information Engineering, Zhejiang A&F University, Hangzhou, 311300, China

    Houkui Zhou

  4. Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou, 311300, China

    Houkui Zhou

Authors
  1. Houkui Zhou
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  2. Huimin Yu
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  3. Roland Hu
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Correspondence to Huimin Yu.

Additional information

Houkui Zhou is a PhD student of the Department of Information Science and Electronic Engineering, Zhejiang University (ZJU), China. He got his bachelor degree from Hangzhou Dianzi University, China in 2003 and his master degree from Department of Information Science and Electronic Engineering, ZJU in 2006. His research interests include cross-media analysis and mining and topic evolution.

Huimin Yu received the PhD degree in communication and electronic systems from the Department of Information Science and Electronic Engineering, Zhejiang University (ZJU), China in 1996. He is currently a professor with the Department of Information Science and Electronic Engineering and the State Key Laboratory of CAD&CG, ZJU. His current research interests include cross-media data mining and analysis, and machine learning.

Roland Hu received the BS degree in electrical engineering from Tsinghua University, China, and the PhD degree in audio-visual person recognition from the University of Southampton, UK in 2002 and 2007, respectively. He was a postdoctoral researcher with the Communications and Remote Sensing Laboratory, Université Catholique de Louvain, Belgium from 2007 to 2009. Since 2009, he has been an assistant professor with the Department of Information Science and Electronic Engineering, Zhejiang University, China. His current research interests include computer vision, image processing, pattern recognition, and digital watermarking.

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Zhou, H., Yu, H. & Hu, R. Topic evolution based on the probabilistic topic model: a review. Front. Comput. Sci. 11, 786–802 (2017). https://doi.org/10.1007/s11704-016-5442-5

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