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
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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2019, Vol. 32 
