Improving Review Representations with User Attention and Product Attention for Sentiment Classification
Table 3: Effect of user attention and product attention. HUA only uses user information and local text, and HPA only uses product information and local text.
['Models', 'IMDB Acc.', 'IMDB RMSE', 'Yelp 2013 Acc.', 'Yelp 2013 RMSE', 'Yelp 2014 Acc.', 'Yelp 2014 RMSE']
To investigate the effects of single user attention or product attention, we also implement independent hierarchical user attention network (HUA) and hierarchical product attention network (HPA). From the table, we can observe that: Compared to the model NSC+LA(BiLSTM) only using local semantic attention, HUA and HPA both achieve some improvements, which validates the rationality of incorporating user and product into sentiment classification via attention mechanism. The results also indicate that user attention or product attention can capture more information related to sentiment. The user information is more effective than the product information to enhance review representations. Although some words or sentences in reviews show product’s characteristic, the ratings are finally decided by users. Hence, it is reasonable that the discrimination of user’s preference is more obvious than product’s characteristic. Compared to single user attention or product attention, our model achieves better performance, which indicates that user and product information both contribute to our model. The results demonstrate our user attention and product attention mechanism can catch the specific user’s preference and product’s characteristic.