Document-level sentiment classification aims to predict user's overall sentiment in a document about a product. However, most of existing methods only focus on local text information and ignore the global user preference and product characteristics. Even though some works take such information into account, they usually suffer from high model complexity and only consider wordlevel preference rather than semantic levels. To address this issue, we propose a hierarchical neural network to incorporate global user and product information into sentiment classification. Our model first builds a hierarchical LSTM model to generate sentence and document representations. Afterwards, user and product information is considered via attentions over different semantic levels due to its ability of capturing crucial semantic components. The experimental results show that our model achieves significant and consistent improvements compared to all state-of-theart methods. The source code of this paper can be obtained from https://github. com/thunlp/NSC.
REF enhance the attention with the support of user preference and product information to comprehend how user and product affect sentiment ratings.
Neural Sentiment Classification with User and Product Attention
EMNLP
Computer Science