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.
@article{arxiv.1804.06536,
title = {Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks},
author = {Binxuan Huang and Yanglan Ou and Kathleen M. Carley},
journal= {arXiv preprint arXiv:1804.06536},
year = {2018}
}