Data for the Rating Prediction (RP) sentiment analysis task such as star reviews are readily available. However, data for aspect-category sentiment analysis (ACSA) is often desired because of the fine-grained nature but are expensive to collect. In this work we present a method for learning ACSA using only RP labels. We propose Unified Sentiment Analysis (Uni-SA) to efficiently understand aspect and review sentiment in a unified manner. We propose a Distantly Supervised Pyramid Network (DSPN) to efficiently perform Aspect-Category Detection (ACD), ACSA, and OSA using only RP labels for training. We evaluate DSPN on multi-aspect review datasets in English and Chinese and find that with only star rating labels for supervision, DSPN performs comparably well to a variety of benchmark models. We also demonstrate the interpretability of DSPN’s outputs on reviews to show the pyramid structure inherent in document level end-to-end sentiment analysis.

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. 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 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 field 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.