Aspect-Based Sentiment Classification with Background Information and Syntactic Auxiliary Tasks

Abstract

Aspect-based sentiment classification is the task of predicting the sentiment tendency of a text toward a given aspect. Existing works on this task mainly focus on aspect-relevant information. In contrast, we design a model (BAT) which could extract overall Background information as well as Aspect-relevant informaTion. To make the BAT model learn better semantic representation of the given text, we introduce two auxiliary tasks (dependency neighborhood prediction and part-of-speech tagging). These auxiliary tasks are used to train the model together with the main sentiment classification task. Experiments on three benchmark datasets demonstrate that our method is effective and the proposed model achieves substantial performance improvements over comparison models.