Aspect-based sentiment analysis with gated alternate neural network

Abstract

Aspect-based sentiment analysis (ABSA) is a type of fine-grained sentiment analysis. Previous work in ABSA is mostly based on recurrent neural networks (RNNs). However, RNNs employed in ABSA have some weaknesses, such as lacking position invariance and lacking sensitivity to local key patterns. Meanwhile, a convolutional neural network (CNN) addresses the limitations in RNN, but itself is weak at capturing long-distance dependency and modeling sequence information. Moreover, the attention mechanism employed in ABSA may introduce some noise that is detrimental to capturing important sentiment expressions. In this paper, we assume that a sentence consists of some sentiment clues, and a sentence clue consists of multiple words. Based on this, we propose a novel neural network structure, named the Gated Alternate Neural Network (GANN), to address the limitations mentioned above. In GANN, a specially designed module, named the Gate Truncation RNN (GTR), is used to learn informative aspect-dependent sentiment clue representations. In these representations, the relative distance between each context word and aspect target, the sequence information, and semantic dependency within a sentiment clue are concurrently encoded. To filter out noise, a gating mechanism is designed to control information flow to obtain more precise representations. Convolution and pooling mechanisms are employed to capture key local sentiment clue features and acquire the position invariance of features. To verify the effect and generalization of GANN, we conducted abundant experiments on four Chinese and three English datasets. The experimental results show that GANN achieves state-of-the-art results and indicate that our proposed model is language-independent.