  As aspect-level sentiment labels are expensive and labor-intensive to
acquire, zero-shot aspect-level sentiment classification is proposed to learn
classifiers applicable to new domains without using any annotated aspect-level
data. In contrast, document-level sentiment data with ratings are more easily
accessible. In this work, we achieve zero-shot aspect-level sentiment
classification by only using document-level reviews. Our key intuition is that
the sentiment representation of a document is composed of the sentiment
representations of all the aspects of that document. Based on this, we propose
the AF-DSC method to explicitly model such sentiment composition in reviews.
AF-DSC first learns sentiment representations for all potential aspects and
then aggregates aspect-level sentiments into a document-level one to perform
document-level sentiment classification. In this way, we obtain the
aspect-level sentiment classifier as the by-product of the document-level
sentiment classifier. Experimental results on aspect-level sentiment
classification benchmarks demonstrate the effectiveness of explicit utilization
of sentiment composition in document-level sentiment classification. Our model
with only 30k training data outperforms previous work utilizing millions of
data.

Computation and Language;Artificial Intelligence