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Focused on the issue that text information cannot be fully extracted by the single-channel neural network model, the Dual-Channel Attention Model (DCAM) is proposed for text sentiment analysis. Firstly, text is represented in the form of a matrix using a word vector trained by Word2Vec. Secondly, the matrix is used as input data and sent to Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks for feature extraction. Thirdly, an attention model is introduced to extract important feature information. Finally, the text features are merged, and the classification layer is used to classify the sentiment. The model is evaluated on a Chinese corpus. According to the experimental results, the accuracy of the proposed model can reach 92.7%, which is obviously superior to other single-channel neural network models. [ABSTRACT FROM AUTHOR]