Lifelong Learning Memory Networks for Aspect Sentiment Classification

Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. It aims at classifying the sentiment expressed on some target aspects/features of entities (e.g., products and services). Although a great deal of research has been done, this task remains to be very challenging. Recently, memory networks, a type of neural model, have been used for this task and have achieved state-of-the-art results. However, such neural models usually require a large amount of well-annotated training data for producing reasonably good results. Unfortunately, for the ASC task, the human-annotated data with aspect-level labels are scarce and costly to obtain. In this work, we aim to use big unlabeled data to help. The key idea is to make a memory network learn knowledge from the big unlabeled data (treated as past tasks) and use the learned knowledge to better guide its future task learning. To achieve this goal, we propose a novel lifelong learning approach that can automatically meta-mine knowledge from multiple past domains. In addition, a new model named lifelong learning memory network (L2MN) is proposed to incorporate the mined knowledge into its learning process, where two types of knowledge are involved, namely, aspect-sentiment attention and context-sentiment effect. Extensive experimental results using real-world review datasets demonstrate the effectiveness of our approach.