ZOU B R, WANG Y C, WANG W D, et al.CNN-BICIFG-Attention model for text sentiment classification[J].Journal of Henan Polytechnic University(Natural Science) ,2022,41(6):155-162.

doi:10.16186/j.cnki.1673-9787.2021060007

Received:2021/06/02

Revised:2021/07/10

Published:2022/11/25

CNN-BICIFG-Attention model for text sentiment classification

ZOU Borong1, WANG Yicheng2, WANG Weidong1, HOU Qinghua1, WU Huibin1

1.School of Physics and Electronic Information，Henan Polytechnic University，Jiaozuo  454000，Henan，China；2.School of Electrical Engineering and Automation，Henan Polytechnic University，Jiaozuo  454000，Henan，China

Abstract:Aiming at the problems of incomplete extraction of complex information features of text data sets in processing Chinese short text sentiment classification tasks，and insufficient learning of contextual texts in common neural networks，a dual-channel composite network model combined with attention mechanism was proposed.The corpus was preprocessed to form a text vector matrix.In the two channels，the convolutional neural network layer，the bidirectional coupling input and the forgetting gate network layer were used to extract the local feature information of the sample vector to learn the connection between the previous and the next word vectors； Then，the attention mechanism network layer was added separately to assign weights to the text information of different emotion densities for improving the intensity of the impact of key information on the sentiment classification of sentences； Finally，the two channel feature vectors were merged.The multi-layer hybrid network model proposed in this paper was tested on the crawled Jingdong product review set and SogouCS data set，the accuracy rate reached 93.17%，and the F-SCORE value reached 93.12%.The results verified the effectiveness of the dual-channel composite model.

Key words:sentiment classification;convolutional neural network;two-way coupling input and forget gate network;attention mechanism;accuracy;F-SCORE value

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