Past work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction. Firstly, we incorporate all available historical review text belonging to the author of the review in question. Secondly, we investigate the inclusion of historical reviews associated with the current product (written by other users). We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product. Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other. Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.

该论文研究了将历史评论文本和用户/产品信息纳入情感分析模型，通过强制模型记住有关特定用户和产品的所有评论，通过舍弃分层的架构来使文本中的单词直接彼此关注。在IMDB、Yelp2013 和 Yelp2014数据集上的实验结果表明，该方法在某些情况下可以实现超过2％的现有最佳结果的提升。

利用用户和产品背景提高文档级情感分析