Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (19): 176-181.DOI: 10.3778/j.issn.1002-8331.1907-0146

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Multi-layered Attention Network for Aspect-Level Sentiment Classification

ZHENG Cheng, CAO Yuan, XUE Manyi   

  1. School of Computer Science and Technology, Anhui University, Hefei 230601, China
  • Online:2020-10-01 Published:2020-09-29



  1. 安徽大学 计算机科学与技术学院,合肥 230601


A sentiment classification that is specific to one aspect is a fine-grained task in the field of sentiment analysis. Deep neural networks can better extract context features and aspect features, and use the attention mechanism to assign corresponding weight values according to the different importance of context features and aspect features. The model focuses on extracting context and aspect features and better integrating context and aspect vectors, and proposes a deep neural network with mixed extraction and multi-layer attention. Based on Bi-LSTM and CNN, there are significant results in extracting features, and a merge model of two networks is introduced. Finally, it is verified on the classic Laptop, Resteraunt and Twitter datasets, showing a better classification effect than the benchmark model.

Key words: aspect-level, sentiment classification, multi-layer attention



关键词: 方面级, 情感分类, 多层注意