A convolutional attentional neural network for sentiment classification

Jiachen Du, Lin Gui, Yulan He, Ruifeng Xu

Research output: Unpublished contribution to conferenceOtherpeer-review


Neural network models with attention mechanism have shown their efficiencies on various tasks. However, there is little research work on attention mechanism for text classification and existing attention model for text classification lacks of cognitive intuition and mathematical explanation. In this paper, we propose a new architecture of neural network based on the attention model for text classification. In particular, we show that the convolutional neural network (CNN) is a reasonable model for extracting attentions from text sequences in mathematics. We then propose a novel attention model base on CNN and introduce a new network architecture which combines recurrent neural network with our CNN-based attention model. Experimental results on five datasets show that our proposed models can accurately capture the salient parts of sentences to improve the performance of text classification.
Original languageEnglish
Publication statusE-pub ahead of print - 1 Mar 2018
Event2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) - Shenzhen, China
Duration: 15 Dec 201717 Dec 2017


Conference2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

Bibliographical note

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Funding: National Natural Science Foundation of China 61370165, U1636103, 61632011, Shenzhen Foundational Research Funding
JCYJ20150625142543470, 20170307150024907, Guangdong
Provincial Engineering Technology Research Center for Data
Science 2016KF09.


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