In order to solve the problem that the existing deep learning method has insufficient ability in feature extraction in the text emotion classification task, this paper proposes a text emotion analysis using the dual-channel convolution neural network in the social network. First, a double-channel convolutional neural network is constructed. Combined with emotion words, parts of speech, degree adverbs, negative words, punctuation, and other word features that affect the text’s emotional tendency, an extended text feature is formed. Then, using the CNN’s multichannel mechanism, the extended text features based on the word vector features and the semantic features based on the word vectors are, respectively, input into the CNN model. After each convolution operation of the convolution channel, the BN technology is used to normalize the internal data of the network and the padding technology is used to improve the ability of the model to extract edge features of the data and the speed of the model. Finally, a dynamic k-max continuous pooling strategy is adopted to realize the dimensionality reduction of features and enhance the model’s ability to extract features. The experimental results show that the accuracy and F1 values obtained by the proposed method can be as high as 94.16% and 92.61%, respectively, which are better than several comparison algorithms.
Research on software credibility algorithm based on deep convolutional sparse coding
