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Multi-modal multi-layered topic classification model for social event analysis

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Abstract

In this paper, we pay attention to reveal the event topics and track the evolutionary trend of social event and a novel probabilistic topic model is proposed. The Multi-modal Multi-layered Topic Classification Model (tm_MMC) for Social Event Analysis has the capacity for revealing visual and non-visual topics, by jointly modeling the textual and visual information while simultaneously learning and predicting the multi-layered category labels. In order to track the evolutionary trends of the topics online, tm_MMC uses topic intensity and heritability to incrementally build an up-to-date model. To evaluate the effectiveness of our model, we experiment using a collected data, and compare the results with those of other traditional models. The results demonstrate the effectiveness and advantages of our model against several state-of-the-art methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China No.61303131, No. 61672272, No. 60973040; Educational Reform Project of Fujian No.FBJG20170055.

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Authors and Affiliations

  1. College of Computer Science, Minnan Normal University, Zhangzhou, Fujian, 363000, China

    Y. H. Chen & Y. J. Lin

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, China

    Y. H. Chen & Y. J. Lin

  3. School of Journalism and Communication, Minnan Normal University, Zhangzhou, Fujian, 363000, China

    C. Y. Yin

  4. College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China

    W. L. Zuo

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  1. Y. H. Chen
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  2. C. Y. Yin
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Correspondence to Y. H. Chen.

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Chen, Y.H., Yin, C.Y., Lin, Y.J. et al. Multi-modal multi-layered topic classification model for social event analysis. Multimed Tools Appl 77, 23291–23315 (2018). https://doi.org/10.1007/s11042-017-5588-7

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  • DOI: https://doi.org/10.1007/s11042-017-5588-7

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