ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction
This provides a new dataset and model for sentiment analysis in e-commerce, specifically for Chinese restaurant reviews, but it is incremental as it builds on existing tasks without fundamentally changing the field.
The authors introduced ASAP, a large-scale Chinese restaurant review dataset with 46,730 reviews annotated for 18 aspect categories and overall ratings, to address the lack of joint datasets for aspect category sentiment analysis and rating prediction. They proposed a joint model that outperformed state-of-the-art baselines on both tasks.
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.