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In this paper, we propose document-level end-to-end sentiment analysis toefficiently understand aspect and review sentiment expressed in online reviewsin a unified manner. In particular, we assume that star rating labels are a"coarse-grained synthesis" of aspect ratings across in the review. We propose aDistantly Supervised Pyramid Network (DSPN) to efficiently performAspect-Category Detection, Aspect-Category Sentiment Analysis, and RatingPrediction using only document star rating labels for training. By performingthese three related sentiment subtasks in an end-to-end manner, DSPN canextract aspects mentioned in the review, identify the corresponding sentiments,and predict the star rating labels. We evaluate DSPN on multi-aspect reviewdatasets in English and Chinese and find that with only star rating labels forsupervision, DSPN can perform comparably well to a variety of benchmark models.We also demonstrate the interpretability of DSPN's outputs on reviews to showthe pyramid structure inherent in document level end-to-end sentiment analysis.

Stars Are All You Need: A Distantly Supervised Pyramid Network for Document-Level End-to-End Sentiment Analysis