Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities. %Considering the sentiment of the aspects(ACSA) and the overall review rating(RP) simultaneously has the potential to improve the overall performance. ACSA and RP are highly correlated and usually employed jointly in real-world e-commerce scenarios. While most public datasets are constructed for ACSA and RP separately, which may limit the further exploitation of both tasks. To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset \textbf{ASAP} including $46,730$ genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China. Besides a $5$-star scale rating, each review is manually annotated according to its sentiment polarities towards $18$ pre-defined aspect categories. We hope the release of the dataset could shed some light on the fields of sentiment analysis. Moreover, we propose an intuitive yet effective joint model for ACSA and RP. Experimental results demonstrate that the joint model outperforms state-of-the-art baselines on both tasks.

本文介绍了一项有关餐厅评论数据的研究，数据集包含了46,730条来自中国领先在线到线下（O2O）电子商务平台的餐厅评论，每条评论都按照其对18个预定义方面类别的情感极性进行手动注释，并提出了一种直观而有效的联合模型用于方面类别情感分析和评论评级预测，实验结果证明，该联合模型优于目前的基线模型。

ASAP: 面向方面分类情感分析和评级预测的中文评论数据集