  Aspect-category sentiment analysis (ACSA) aims to predict the aspect
categories mentioned in texts and their corresponding sentiment polarities.
Some joint models have been proposed to address this task. Given a text, these
joint models detect all the aspect categories mentioned in the text and predict
the sentiment polarities toward them at once. Although these joint models
obtain promising performances, they train separate parameters for each aspect
category and therefore suffer from data deficiency of some aspect categories.
To solve this problem, we propose a novel joint model which contains a shared
sentiment prediction layer. The shared sentiment prediction layer transfers
sentiment knowledge between aspect categories and alleviates the problem caused
by data deficiency. Experiments conducted on SemEval-2016 Datasets demonstrate
the effectiveness of our model.
