Authors: Qingqing Wang; Jianglin Luo; Jianwen Song
Addresses: Jilin Animation Institute, Changchun 130012, Jilin, China ' Jilin Animation Institute, Changchun 130012, Jilin, China ' Jilin Animation Institute, Changchun 130012, Jilin, China; China Academy of Art, Hangzhou 310002, Shanghai, China
Abstract: On the basis of affective computing technology and deep learning, this paper proposes a hybrid neural network model based on CNN-BGRU to solve the problem of accurate classification. In this algorithm, firstly, the convolution neural network is used to extract the local features of the input text vector, and then BGRU is used to obtain the information before and after this layer, and then the global features are obtained. Finally, the emotion classification results are obtained by Softmax classifier. The experimental results show that the accuracy of the proposed algorithm is 92.8%, the lowest loss rate is 0.2, and the trend is stable. It can be seen that this model can not only obtain more semantic information between texts, but also better capture the dependence of specific emotions in the whole text, so as to more effectively identify the emotional polarity in different aspects of the text.
Keywords: elderly alone; aged-care at home; convolutional neural network; BGRU; emotional analysis; deep learning.
International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.4, pp.352 - 362
Received: 20 Aug 2020
Accepted: 11 Nov 2020
Published online: 02 Sep 2021 *