  Data for the Rating Prediction (RP) sentiment analysis task such as star
reviews are readily available. However, data for aspect-category detection
(ACD) and aspect-category sentiment analysis (ACSA) is often desired because of
the fine-grained nature but are expensive to collect. In this work, we propose
Unified Sentiment Analysis (Uni-SA) to understand aspect and review sentiment
in a unified manner. Specifically, we propose a Distantly Supervised Pyramid
Network (DSPN) to efficiently perform ACD, ACSA, and RP using only RP labels
for training. We evaluate DSPN on multi-aspect review datasets in English and
Chinese and find that in addition to the internal efficiency of sample size,
DSPN also 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 unified sentiment analysis.
