Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks

This paper introduces a new method to classify sentiment polarity for aspects in product reviews. We call it bitmask bidirectional long short term memory networks. It is based on long short term memory (LSTM) networks, which is a frequently mentioned model in natural language processing. Our proposed method uses a bitmask layer to keep attention on aspects. We evaluate it on reviews of restaurant and laptop domains from three popular contests: SemEval-2014 task 4, SemEval-2015 task 12, and SemEval-2016 task 5. It obtains competitive results with state-of-the-art methods based on LSTM networks. Furthermore, we demonstrate the benefit of using sentiment lexicons and word embeddings of a particular domain in aspect-based sentiment analysis.