论文标题
通过用户和产品上下文改进文档级别的情感分析
Improving Document-Level Sentiment Analysis with User and Product Context
论文作者
论文摘要
通过编码用户和产品信息来改善文档级别情绪分析的过去工作仅限于考虑当前审查的文本。我们调查了在情感预测时可用的其他审核文本，这对于指导预测有意义。首先，我们将属于该评论的作者的所有可用历史评论文本结合在一起。其次，我们调查了与当前产品相关的历史评论（由其他用户编写）。我们通过明确存储同一用户和相同产品编写的评论的表示形式来实现这一目标，并迫使模型记住一个特定用户和产品的所有评论。此外，我们放弃了以前工作中使用的层次结构，以启用文本中的单词以直接互相参加。在最佳情况下，IMDB，Yelp 2013和Yelp 2014数据集的实验结果显示，最先进的时间超过2个百分点。
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.
