Research Article
Research on Text Sentiment Analysis Based on Attention C_MGU
@INPROCEEDINGS{10.1007/978-3-030-64214-3_11, author={Diangang Wang and Lin Huang and Xiaopeng Lu and Yan Gong and Linfeng Chen}, title={Research on Text Sentiment Analysis Based on Attention C_MGU}, proceedings={Mobile Computing, Applications, and Services. 11th EAI International Conference, MobiCASE 2020, Shanghai, China, September 12, 2020, Proceedings}, proceedings_a={MOBICASE}, year={2020}, month={12}, keywords={Sentiment analysis C_MGU Attention mechanism}, doi={10.1007/978-3-030-64214-3_11} }- Diangang Wang
Lin Huang
Xiaopeng Lu
Yan Gong
Linfeng Chen
Year: 2020
Research on Text Sentiment Analysis Based on Attention C_MGU
MOBICASE
Springer
DOI: 10.1007/978-3-030-64214-3_11
Abstract
Combining the advantages of the convolutional neural network CNN and the minimum gated unit MGU, the attention mechanism is merged to propose an attention C_MGU neural network model. The preliminary feature representation of the extracted text is captured by the CNN’s convolution layer module. The Attention mechanism and the MGU module are used to enhance and optimize the key information of the preliminary feature representation of the text. The deep feature representation of the generated text is input to the Softmax layer for regression processing. The sentiment classification experiments on the public data sets IMBD and Sentiment140 show that the new model strengthens the understanding of the sentence meaning of the text, can further learn the sequence-related features, and effectively improve the accuracy of sentiment classification.
