Sentiment Prediction using Attention on User-Specific Rating Distribution

For document-level sentiment prediction, many methods try to first capture opinion words then infer sentiments based on these words. We observe that different users may use same words to express different levels of satisfaction, e.g., 'great' may mean very satisfaction to some users, or simply a general description to others. Intuitively, we expect the choice of a sentiment expression follows a distribution specific to a user and her sentiment to a product. In this paper, we propose a hierarchical neural network model with user-specific rating distribution attention (H-URA) to learn document representation for sentiment prediction. Our model learns local sentiment distributions from a user's expression, at word-level and at sentence-level respectively. We also learn a global sentiment distribution by using both user and product information. The attention weight is then computed from the local and global sentiment distributions. Experimental results show superiority of our H-URA model compared to strong baselines on benchmark datasets.