Document-level sentiment classification is dedicated to predicting the sentiment polarity of document-level reviews posted by users about products and services. Many methods use neural networks have achieved very successful results on sentiment classification tasks. These methods usually focus on mining useful information from the text of the review documents. However, they ignore the importance of users’ review habits. The reviews posted by the same user when commenting on different products contain similar review habits, and reviews that contain highly similar review habits often have similar sentiment ratings. In this paper, we propose a novel sentiment classification algorithm that utilizes user’s review habits to enhance hierarchical neural networks, namely as HUSN. Firstly, we divide the reviews in the training set according to the users. All the reviews of each user are aggregated together and called the historical reviews of this user. Secondly, the target review in the test set and its multiple historical reviews in the training set are sent to the Long Short-Term Memory based hierarchical neural network to obtain the corresponding review document representations containing the user’s review habits. Finally, we calculate the similarities between the target review document representation and multiple historical review document representations. The higher the similarity, the closer the review habits of different reviews from the same user, and the closer the corresponding sentiment ratings. Experimental results show that the similarities between the review habits of different reviews from the same user can further improve the performance of document-level sentiment classification. The HUSN algorithm performs better than all baseline methods on three publicly available document-level review datasets.

文档级情感分类专用于预测用户发布的有关产品和服务的文档级评论的情感极性。许多使用神经网络的方法在情感分类任务上都取得了非常成功的结果。这些方法通常集中于从审阅文档的文本中挖掘有用的信息。但是，他们忽略了用户评论习惯的重要性。同一用户在对不同产品进行评论时发布的评论包含相似的评论习惯，而包含高度相似的评论习惯的评论通常具有相似的情感评分。在本文中，我们提出了一种新颖的情感分类算法，该算法利用用户的评论习惯来增强分层神经网络，即HUSN。首先，我们根据用户在培训集中划分评论。每个用户的所有评论汇总在一起，称为该用户的历史评论。其次，将测试集中的目标评论及其在训练集中的多个历史评论发送到基于长短期记忆的层次神经网络，以获得包含用户评论习惯的相应评论文档表示。最后，我们计算目标审阅文件表示形式与多个历史审阅文件表示形式之间的相似性。相似度越高，来自同一用户的不同评论的评论习惯越接近，相应的情感评分也越接近。实验结果表明，来自同一用户的不同评论的评论习惯之间的相似性可以进一步提高文档级情感分类的性能。