Weakly-Supervised Left-Center-Right Context-Aware Aspect Category and Sentiment Classification

Aspect-Based Sentiment Analysis (ABSA) aims to extract all aspects mentioned in a Web review and classify the aspect category and sentiment for each aspect. Most existing methods rely on single-task supervised approaches. However, ABSA tasks are not independent. Furthermore, obtaining labeled data might be difficult or expensive. The Context-aware Aspect category and Sentiment Classification (CASC) model addresses this issue by classifying categories and sentiments simultaneously using a weakly-supervised approach. However, CASC uses a simple neural network on the input text that does not exploit any other information. This paper proposes an extension named Left-Center-Right+CASC (LCR+CASC), where we implement a sophisticated neural model that exploits the location of explicit aspect expressions. Besides aspect categorization and sentiment classification, LCR+CASC also extracts target expressions from a sentence, which goes beyond CASC’s abilities. This paper conducts two experiments on restaurant reviews: extracting target expressions and using annotated data that provide targets to evaluate the proposed model. Results show that LCR+CASC outperforms CASC when targets are given, and is able to extract target expressions to some extent.