Document-level review sentiment classification aims to predict the sentiment category for given review documents written by users for products. Most of the existing methods focus on generating a good review document representation and classifying the review document directly. However, on the one hand, as document-level review sentiment classification usually includes many sentiment categories and the difference between these sentiment categories is not obvious, it may be difficult to obtain satisfying result by direct classification. On the other hand, this once classification process with review representation may fail to well interpret how the results are achieved. In addition, although some information such as user preference and product characteristics are incorporated when building models, the interactive information between user and product are usually ignored. In this paper, inspired by the deductive reasoning strategy of human doing multiple choice questions, we are motivated to propose a Two-Stage Sentiment Classification (TSSC) model to classify review documents in two stages: (1) Coarse classification stage, where model mainly adopts user-product interactive information to pre-judge the sentiment tendency of the review document without considering the review information; (2) Fine classification stage, where model uses text information of the review document for further classification based on the sentiment tendency obtained in coarse classification stage. Finally, the sentiment classification task is accomplished by combining both the results of coarse classification and fine classification. The experimental results demonstrate that our TSSC model significantly outperforms most of the related models (e.g., Trigram and NSC+UPA) on IMDB and Yelp datasets in terms of classification accuracy. When compared with the state-of-the-art HUAPA model, our TSSC model not only achieves slightly more accurate performance, but also has lower time complexity and stronger interpretability.

文档级审阅情感分类旨在预测用户为产品编写的给定审阅文档的情感类别。现有的大多数方法都着重于生成良好的审阅文件表示形式并直接对审阅文件进行分类。然而，一方面，由于文档级别的评论情感分类通常包括许多情感类别，并且这些情感类别之间的差异并不明显，因此可能难以通过直接分类来获得令人满意的结果。另一方面，这种带有评论表示的曾经的分类过程可能无法很好地解释结果的实现方式。此外，尽管在构建模型时会合并一些信息（例如用户偏好和产品特征），用户与产品之间的交互信息通常被忽略。在本文中，受人类做多项选择题的演绎推理策略的启发，我们有动机提出一种两阶段情感分类（TSSC）模型，以将评论文件分为两个阶段：（1）粗分类阶段，其中模型主要采用用户产品交互信息来预先判断审阅文档的情感倾向，而不考虑审阅信息；（2）精细分类阶段，其中模型根据粗略分类阶段获得的情感趋势使用评论文档的文本信息进行进一步分类。最后，通过将粗略分类和精细分类的结果相结合来完成情感分类任务。实验结果表明，在分类精度方面，我们的TSSC模型在IMDB和Yelp数据集上明显优于大多数相关模型（例如Trigram和NSC + UPA）。与最新的HUAPA模型相比，我们的TSSC模型不仅可以实现更精确的性能，而且时间复杂度更低，可解释性更高。