S[formula omitted]map: Semisupervised aspect-based sentiment analysis with masked aspect prediction

Abstract

Aspect-based sentiment analysis (ABSA) refers to a fine-grained task of detecting the sentiment polarities of sentences at the aspect level. To resolve this task, ABSA training samples must be annotated with aspect words and the corresponding sentiment polarities. However, collecting such fine-grained training samples is expensive and time-consuming. Therefore the available ABSA training samples are often scarce. To break the data scarcity challenge of ABSA, we investigate semi-supervised aspect-based sentiment analysis (SemiABSA), which trains ABSA models using a limited amount of expensive labeled sentences and more unlabeled-yet-cheaper sentences. We propose a novel SemiABSA framework, namely semi-supervised aspect-based sentiment analysis with masked aspect prediction (S3map), built on the self-training paradigm. We form pseudo-aspect words and pseudo-sentiment polarities for unlabeled sentences and improve model training. Specifically, a BERT-encoder-based masked aspect prediction (MAP) task achieves the pseudo-aspect words generation. Based on S3map, we thoroughly investigate the potential of SemiABSA from various perspectives. The empirical results show that S3map can consistently improve performance by leveraging unlabeled sentences, even those from different domains.