Sentiment Analysis of Aspects Based on Gated Interaction Dilated Convolution Neural Network

• 1、Department of Electronic Engineering, Beijing University of Posts and Telecommunications , Beijing 100876

Abstract：\justifying Attribute sentiment analysis as a fine-grained sentiment analysis problem, its purpose is to clarify the emotion of specific aspect. For the above problems, most of the previous methods are combined with recurrent neural network and BERT to construct the network model. However, recurrent neural network is difficult to parallelize the model due to their time sequence characteristics. In order to solve this problem, combined with the high computational efficiency of gated convolutional networks, combining multi-channel text features and gated interactive dilated convolutional networks is proposed. The gated convolutional network is used as a feature encoder to improve the efficiency of model training. At the same time, using multi-channel text features enhances the feature extraction effect in the context, and then captures the emotional elements corresponding to the entity attribute words. Experiments on the SemEval2014 data set have achieved a high accuracy rate. In addition, compared with DAuM and BERT models in terms of computational efficiency, it has been improved by 3 times and 22 times.

Keywords： \justifying artificial intelligence aspect sentiment analysis gated interactive dilated convolutional networks multi-channel text features computational efficiency