A Density-Based Nonparametric Model for Online Event Discovery from the Social Media Data

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Status	已發表Published
	A Density-Based Nonparametric Model for Online Event Discovery from the Social Media Data
	Guo, J.; Gong, Z. G.
	2017-08-01
Source Publication	Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017
Abstract	In this paper, we propose a novel online event discovery model DP-density to capture various events from the social media data. The proposed model can flexibly accommodate the incremental arriving of the social documents in an online manner by leveraging Dirichlet Process, and a density based technique is exploited to deduce the temporal dynamics of events. The spatial patterns of events are also incorporated in the model by a mixture of Gaussians. To remove the bias caused by the streaming process of the documents, Sequential Monte Carlo is used for the parameter inference. Our extensive experiments over two different real datasets show that the proposed model is capable to extract interpretable events effectively in terms of perplexity and coherence.
Keyword	Learning Graphical Models Time-series/Data Streams Approximate Probabilistic Inference
URL	View the original
Language	英語English
The Source to Article	PB_Publication
PUB ID	32573
Document Type	Conference paper
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding Author	Gong, Z. G.
Recommended Citation GB/T 7714	Guo, J.,Gong, Z. G.. A Density-Based Nonparametric Model for Online Event Discovery from the Social Media Data[C], 2017.
APA	Guo, J.., & Gong, Z. G. (2017). A Density-Based Nonparametric Model for Online Event Discovery from the Social Media Data. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017.

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