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.
Science and technology projects funded by State Grid Sichuan Electric Power Company (NO.: 521947140005).
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Wang, D., Huang, L., Lu, X., Gong, Y., Chen, L. (2020). Research on Text Sentiment Analysis Based on Attention C_MGU. In: Liu, J., Gao, H., Yin, Y., Bi, Z. (eds) Mobile Computing, Applications, and Services. MobiCASE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-64214-3_11
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