Multi-way matching based fine-grained sentiment analysis for user reviews

被引：1
|
作者：
Guo, Xin [1 ]
Zhang, Geng [1 ]
Wang, Suge [1 ,2 ]
Chen, Qian [1 ,2 ]
机构：
[1] School of Computer and Information Technology, Shanxi University, Taiyuan,Shanxi, China
[2] Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan,Shanxi, China
来源：
Neural Computing and Applications | 2020年 / 32卷 / 10期
关键词：
While sentiment analysis has been widely used in public opinion to explore tendency of users for a target product from large online review data; less work focus on aspect-level or fine-grained sentiment analysis in which the polarity of not only the aspect of a target object but also the attribute of that given aspect should be determinated. Recent work regards aspect-level sentiment analysis as two separate tasks; i.e; aspect classification and sentiment analysis; and this pipeline method leads to error propagation. To address this issue; this paper proposes an improved multi-way matching deep neural network model for fine-grained sentiment analysis; which jointly models the two tasks in one phase and improves current attention by directly capturing past attention in the multi-round alignment architecture; so as to prevent error propagation and attention deficiency problems. Experimental results on fine-grained sentiment analysis data sets of catering industry indicate that the F1 score of this model in actual test set reaches 0.7302 and EM score 87.1973; which are higher than baseline DocRNN model by 3.8% and 0.88% in F1 and EM; and are higher than SVM by 15.4% and 25.6%; which verified that our model could effectively predict fine-grained sentiment and have better generalization performance. © 2020; Springer-Verlag London Ltd; part of Springer Nature;
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页码：5409 / 5423
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