Convolutional Neural Network and User Information Based Model for Microblog Topic Tracking
FU Peng, LIN Zheng, YUAN Fengcheng, LIN Hailun, WANG Weiping, MENG Dan
National Engineering Laboratory for Information Security Technologies,Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093
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.
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.
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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