Residual Fusion Network Based Attention for Aspect Level Sentiment Analysis

Abstract

Aspect level sentiment analysis is a more fine-grain task compared to document level and sentence level. The aim of the task is to identify the sentiment polarity expressed to the given aspect in the text. In this paper, we propose a residual aspect fusion network with attention for the aspect level sentiment classification. In this network, bidirectional GRU is used to collect semantic information of both sentence words and aspect words, and then attention mechanism is applied to construct aspect vectors for each word in the sentence. In the residual fusion module, the model fuses the aspect information and sentence information together by residual connection structure, in which position weight is applied to utilize the position information of aspect words. At the end of the model, Transformer encode layer is used to extract global feature and fully connected layers are used as the classifier. We train and test the network on SemEval2014 task 4 (Restaurant and Laptop) and Twitter datasets, and the experiments demonstrate that our model performs better than previous methods.