  Aspect category sentiment analysis (ACSA) aims to predict the sentiment
polarities of the aspect categories discussed in sentences. Since a sentence
usually discusses one or more aspect categories and expresses different
sentiments toward them, various attention-based methods have been developed to
allocate the appropriate sentiment words for the given aspect category and
obtain promising results. However, most of these methods directly use the given
aspect category to find the aspect category-related sentiment words, which may
cause mismatching between the sentiment words and the aspect categories when an
unrelated sentiment word is semantically meaningful for the given aspect
category. To mitigate this problem, we propose a Sentence Constituent-Aware
Network (SCAN) for aspect-category sentiment analysis. SCAN contains two graph
attention modules and an interactive loss function. The graph attention modules
generate representations of the nodes in sentence constituency parse trees for
the aspect category detection (ACD) task and the ACSA task, respectively. ACD
aims to detect aspect categories discussed in sentences and is a auxiliary
task. For a given aspect category, the interactive loss function helps the ACD
task to find the nodes which can predict the aspect category but can't predict
other aspect categories. The sentiment words in the nodes then are used to
predict the sentiment polarity of the aspect category by the ACSA task. The
experimental results on five public datasets demonstrate the effectiveness of
SCAN.
