Extracting the Collaboration of Entity and Attribute: Gated Interactive Networks for Aspect Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) is composed of aspect term sentiment analysis (ATSA) and aspect category sentiment analysis (ACSA). In the task of ACSA, some existing methods simply bound the aspect category (entity and attribute) as an integrated whole or adopt a randomly initialized embedding to represent the aspect category, which introduces a defective representation of aspect and leads to the ignorance of independent contextual sentiment of entity and attribute. Some other methods only consider the entity and disregard the attribute in predicting the sentiment polarity of aspect category, which leads to the ignorance of the collaboration between the entity and attribute. To this end, we propose a Gated Interactive Network (GIN) for aspect category sentiment analysis in this paper. To be specific, for each context and the corresponding aspect, we adopt two attention-based networks to learn the contextual sentiment for the entity and attribute independently and interactively. Further, based on the interactive attentions learned from entities and attributes, the coordinative gate units are exploited to reconcile and purify the sentiment features for the aspect sentiment prediction. Experimental results on two benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance in the task of ACSA.