基于知识图谱与循环注意力网络的视角级情感分析

doi:10.16451/j.cnki.issn1003-6059.202006001

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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.

Key words： Aspect Level Sentiment Analysis Knowledge Graph Attention Mechanism Deep Learning

Received: 06 March 2020

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61976054,61772135,U1605251), National Natural Science Youth Fund Project(No.41801324), Natural Science Foundation of Fujian Province(No.2017J01755), Open Project of National Laboratory of Pattern Recognition in China(No.201900041)

Corresponding Authors: LIAO Xiangwen, Ph.D., associate professor. His research interests include opinion mining, sentiment ana-lysis and natural language processing.

About author:: DENG Liming, master student. His research interests include opinion mining, sentiment analysis and natural language proce-ssing. WU Yunbing, master, associate professor. His research interests include machine lear-ning, data mining and knowledge representation. YU Xiaoyan, master, lecturer. Her research interests include Chinese information processing.

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	DENG Liming
	WEI Jingjing
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	LIAO Xiangwen

Cite this article:

DENG Liming,WEI Jingjing,WU Yunbing等. Aspect Level Sentiment Analysis Based on Knowledge Graph and Recurrent Attention Network[J]. , 2020, 33(6): 479-487.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202006001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I6/479

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