Supervised and Few-Shot Learning for Aspect-Based Sentiment Analysis of Instruction Prompt

Abstract

Aspect-based sentiment analysis (ABSA), which aims to extract aspects and corresponding opinions from sentences and determine aspect sentiment polarity, has been widely studied in recent years. Most approaches focus on the subtasks of ABSA and deal with them in the pipeline method or end-to-end method. However, these approaches ignore the semantic information of the labels and the correlation between the labels. In this study, we process various ABSA tasks in a unified generative framework and use instruction prompts to guide the generative model to learn the relationships between different sentiment elements, accurately identify the sentiment elements in sentences, and improve the performance of the model in few-shot learning. Experimental results on several benchmark datasets show that our approach achieves significant performance gains. Among them, for the aspect term extraction and sentiment classification task on the Laptop 14 dataset, our method improves the F1 score by 4.08% and 1.87% on fully supervised learning compared to the GAS model and PARA model, respectively. In few-shot learning, we can achieve 80% of the fully supervised learning performance using one-tenth of the dataset. Our method can effectively address the problem of data shortage in low-resource environments.