Luqi Yan1, Jin Han1,*, Yishi Yue2, Liu Zhang2, Yannan Qian3
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 51-65, 2021, DOI:10.32604/cmc.2021.016920
Abstract Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks (CNNs). However, most of these CNN models focus only on learning local features while ignoring global features. In this paper, based on traditional densely connected convolutional networks (DenseNet), a parallel DenseNet is proposed to realize sentiment analysis of short texts. First, this paper proposes two novel feature extraction blocks that are based on DenseNet and a multi-scale convolutional neural network. Second, this paper solves the problem of ignoring global features in traditional CNN models by combining the… More >
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