Recent work has used them to improve the overall performance (Chen et al., 2016;Dong et al., 2017), interpretability (Amplayo et al., 2018a;Angelidis and Lapata, 2018), and personalization (Ficler and Goldberg, 2017) of neural network models in different tasks such as sentiment classification (Tang et al., 2015), review summarization (Yang et al., 2018a), and text generation (Dong et al., 2017).
user and product information have been widely incorporated in sentiment classification models, especially since they are important metadata attributes found in review websites.