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| Aspect Level Sentiment Analysis Based on Recurrent Neural Network with Auxiliary Memory |
| LIAO Xiangwen1,2,3, LIN Wei1,2,3, WU Yunbing1,2,3, WEI Jingjing4, CHEN Guolong4 |
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116;
2.Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University, Fuzhou 350116;
3.Digital Fujian Institute of Financial Big Data, Fuzhou 350116;
4.College of Electronics and Information Science, Fujian Jiang-xia University, Fuzhou 350108 |
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Abstract Aspect level sentiment analysis employs information of terms to extract features from a sentence, and it cannot utilize information of both aspects and terms simultaneously. Therefore, the model performance is low. Aiming at this problem, an aspect level sentiment analysis based on recurrent neural network with auxiliary memory is proposed. Deep bidirectional long short term memory(DBLSTM) and positional information of words are exploited to build position-weighted memory. The attention mechanism is combined with aspect terms to build aspect memory, and with position-weighted memory and aspect memory to input a multi-layer gated recurrent unit. Then, sentimental features of the aspect are obtained. Finally, sentimental polarity is identified by the normalized function. Experimental results show that the proposed method achieves better results on three public datasets with high effectiveness.
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Received: 24 June 2019
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| Fund:Supported by National Natural Science Foundation of China(No.61772135,U1605251), Natural Science Foundation of Fujian Province(No. 2017J01755), Open Project of Key Laboratory of Network Data Science & Technology of Chinese Academy of Sciences(No. CASNDST201708,CASNDST201606), Open Project of National Laboratory of Pattern Recognition in China ( No.201900041), CERNET Innovation Project(No. NGII20160501), Directors Project Fund of Key Laboratory of Trustworthy Distributed Computing and Service of Ministry of Education (No.2017KF01) |
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Corresponding Authors:
LIAO Xiangwen, Ph.D., professor. His research interests include opinion mining and sentiment analysis.
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| About author:: LIN Wei, master student. His research interests include opinion mining and sentiment analysis.WU Yunbing, master, associate professor. His research interests include knowledge re-presentation and knowledge discovery.WEI Jingjing, Ph.D., lecturer. Her research interests include opinion mining. |
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2019, Vol. 32 
