  Aspect-category sentiment analysis (ACSA) aims to predict sentiment
polarities of sentences with respect to given aspect categories. To detect the
sentiment toward a particular aspect category in a sentence, most previous
methods first generate an aspect category-specific sentence representation for
the aspect category, then predict the sentiment polarity based on the
representation. These methods ignore the fact that the sentiment of an aspect
category mentioned in a sentence is an aggregation of the sentiments of the
words indicating the aspect category in the sentence, which leads to suboptimal
performance. In this paper, we propose a Multi-Instance Multi-Label Learning
Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats
sentences as bags, words as instances, and the words indicating an aspect
category as the key instances of the aspect category. Given a sentence and the
aspect categories mentioned in the sentence, AC-MIMLLN first predicts the
sentiments of the instances, then finds the key instances for the aspect
categories, finally obtains the sentiments of the sentence toward the aspect
categories by aggregating the key instance sentiments. Experimental results on
three public datasets demonstrate the effectiveness of AC-MIMLLN.
