Aspect sentiment analysis based on a hierarchical attention network

Abstract

Aspect sentiment analysis based on deep learning is one of the hot spots in natural language processing.Aiming at aspect sentiment,a deep hierarchical attention network model based on aspect sentiment analysis was proposed.The local features of the text and the temporal relationship of different sentences were retained in model through the convolutional neural network,and the emotional features within and between sentences were obtained by using the layered long shortterm memory network (LSTM).Among them,specific aspects of information were added to LSTM and a dynamic control chain was designed to improve the traditional LSTM.A comparative experiment is conducted on the two data sets in SemEval 2014 and the Twitter data set.Compared with the traditional model,the accuracy of sentiment classification of the proposed model increases by about 3%.

Keywords

• deep learning;aspect sentiment;regional convolutional neural network;layered long short-term memory;attention mechanism;dynamic control chain