Analysis of targeted sentiment by the attention gated convolutional network model

The recurrent neural networks are used for traditional targeted sentiment analysis and usually lead to a long training time. And other alternative models are unable to make a good interaction between context and target words. An attention gated convolutional network model for targeted sentiment analysis is proposed. First, context and target words are processed by the multiple attention mechanism to enhance their interactions. Second, the gated convolution mechanism is used to selectively generate emotional features. Finally, the emotional features are classified by the Softmax classifier to output the emotional polarity. Experimental results show that compared with the Recurrent Attention Network model, which has the highest accuracy rate in the recurrent neural network models, the proposed model improves the accuracy rate by 1.29% and 0.12% respectively on the Restaurant and Laptop datasets of SemEval 2014 Task4. Compared with the Attention-based Long Short-Term Memory Network model, which has a faster convergence rate in the recurrent neural network model, the convergence time is reduced by 29.17 s.