Natural Language Emotion Analysis Model Based on dual attention Convolutional Neural Networks

Convolutional Neural Networks (CNN) cannot distinguish the correlation between feature words and emotion in input text. Therefore, a dual attention CNN (DACNN) model is proposed that includes two channels: word feature channel and part of speech feature channel. After the feature representation of each channel is obtained, the two features are fused at the global attention level. Finally, the generated text feature representation is used as the input of the full connection layer to obtain the final emotional orientation. The model is verified on MR and SST-1 data sets, and the accuracy is improved by 0.7% and 1% respectively compared with conventional CNN and traditional machine learning methods.