Semi-Supervised Learning for Aspect-Based Sentiment Analysis

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Aspect-based sentiment analysis is a rapidly growing domain in natural language processing which is a fine-grained study. Within this broad field, most existing studies use large amounts of labeled data by deep learning methods. However, obtaining massive quantities of labeled data to train a deep neural network model is frequently time-consuming and laborious. In this paper, we focus on semi-supervised learning based on ACSA with few labeled data in restaurant reviews and scholarly paper reviews. In order to leverage information from unlabeled data, the semi-supervised learning method-Ladder network is proposed to fix the problem. Furthermore, the pre-trained language models BERT, ALBERT and Longformer are used for text pre-processing and feature extraction. Extensive experiments on both datasets demonstrate the superiority of the Longformer based Ladder Network compared with supervised learning methods and other semi-supervised learning methods including $\Gamma$ -Model and VAT.

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