Multi-Granularity Position-Aware Convolutional Memory Network For Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis problem, which has received more and more attention in recent years. Convolutional Neural Networks and their variants have shown potentialities for tackling the problem recently. Building upon this line of research, we propose a novel architecture named Multi-Granularity Position-Aware Convolutional Memory Network (MP-CMN) for ABSA in this paper. MP-CMN utilizes multiple convolutional layers to extract features of different granularities to build the convolutional memories, and then incorporates aspect information and position information into convolutional memory network via attention mechanism. To make the mechanism of our model clear, we also make some visualization and case studies. Experiment results on standard SemEval 2014 datasets demonstrate the effectiveness of the proposed model.