  Aspect-level sentiment classification aims to identify the sentiment
expressed towards some aspects given context sentences. In this paper, we
introduce an attention-over-attention (AOA) neural network for aspect level
sentiment classification. Our approach models aspects and sentences in a joint
way and explicitly captures the interaction between aspects and context
sentences. With the AOA module, our model jointly learns the representations
for aspects and sentences, and automatically focuses on the important parts in
sentences. Our experiments on laptop and restaurant datasets demonstrate our
approach outperforms previous LSTM-based architectures.
