Aspect-Based Sentiment Classification Using Interactive Gated Convolutional Network

Aspect-based sentiment classification aims to detect the sentiment polarity of a target in a given context. Most previous approaches use long short-term memory (LSTM) and attention mechanisms to predict the sentiment polarity of targets, which are usually complex and need more training time. Some previous approaches are based on convolutional neural networks (CNN) and gating mechanisms, which are much simpler, efficient and takes lesser convergence time than LSTM due to parallelized computations during training. However, such CNN-based networks ignore the separate modeling of targets via context-specific representations. In this paper, we propose a novel interactive gated convolutional network (IGCN) that uses a bidirectional gating mechanism to learn mutual relation between the target and corresponding review context. IGCN also uses positional information of context words with respect to the given target, POS tags, and domain-specific word embeddings for predicting the sentiment of a target. The experimental results on SemEval 2014 datasets show the effectiveness of our proposed IGCN model.