  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.
