Improving Document-Level Sentiment Classification with User-Product Gated Network

Document-level sentiment classification is a fundamental task in Natural Language Processing (NLP). Previous studies have demonstrated the importance of personalized sentiment classification by taking user preference and product characteristics on the sentiment ratings into consideration. The state-of-the-art approaches incorporate such information via attention mechanism, where the attention weights are calculated after the texts are encoded into the low-dimensional vectors with LSTM-based models. However, user and product information may be discarded in the process of generating the semantic representations. In this paper, we propose a novel User-Product gated LSTM network (UP-LSTM), which incorporates user and product information into LSTM cells at the same time of generating text representations. Therefore, UP-LSTM can dynamically produce user- and product-aware contextual representations of texts. Moreover, we devise another version of it to improve the training efficiency. We conduct a comprehensive evaluation with three real world datasets. Experimental results show that our model outperforms previous approaches by an obvious margin.