Aspect Level Sentiment Analysis Based on Knowledge Graph and Recurrent Attention Network
DENG Liming1,2,3, WEI Jingjing4, WU Yunbing1,2,3, YU Xiaoyan1,2,3, LIAO Xiangwen1,2,3
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 Jiangxia University, Fuzhou 350108
Abstract：The existing aspect level sentiment analysis methods cannot solve the problem of polysemous word in different contexts. Therefore, a method for aspect level sentiment analysis based on knowledge graph and recurrent attention network is proposed. The text representation of the bidirectional long short-term memory network is integrated with synonym information in knowledge graph using dynamic attention mechanism to obtain the state vector of knowledge perception. To classify aspect level sentiment, the memory content is constructed by combining the location information and inputting the multi-level gated recurrent unit for calculating the sentiment characteristics of aspect terms. The experimental results show that the proposed method achieves better classification results on three open datasets.
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2020, Vol. 33 
