基于卷积神经网络和用户信息的微博话题追踪模型<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201701008

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Abstract Aiming at feature sparseness and feature extraction of microblog text, a topic tracking model for Chinese microblog based on convolutional neural network(CNN-TTM) is proposed. Furthermore, user profiles and attributes are incorporated into CNN-TTM and a model called CNN-UserTTM is constructed. The user information of microblog is used to improve the accuracy of topic tracking. The experimental results demonstrate that CNN-TTM and CNN-UserTTM reach a high accuracy respectively on Sina microblog dataset.

Key words： Topic Tracking Convolutional Neural Network Word Vector Microblog Classification User Profile

Received: 19 September 2016

ZTFLH:

TP 391.1

About author:: FU Peng, born in 1988, Ph.D. candidate. His research interests include natural language processing and machine learning. LIN Zheng(Corresponding author), born in 1984, Ph.D., assistant professor. Her research interests include natural language processing and data mining. YUAN Fengcheng, born in 1992, Ph.D. candidate. His research interests include na-tural language processing and deep learning. LIN Hailun, born in 1987, Ph.D., assistant professor. Her research interests include open knowledge computing and information extraction. WANG Weiping, born in 1975, Ph.D., professor. His research interests include big data storage and management and data analysis. MENG Dan, born in 1965, Ph.D., professor. His research interests include big data storage and management and parallel computing.

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	FU Peng
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	WANG Weiping
	MENG Dan

Cite this article:

FU Peng,LIN Zheng,YUAN Fengcheng等. Convolutional Neural Network and User Information Based Model for Microblog Topic Tracking[J]. , 2017, 30(1): 73-80.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201701008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I1/73

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