Multi-prompt Learning Based Aspect-Category Sentiment Analysis

Abstract

Aspect-category sentiment analysis (ACSA) aims to discern aspect categories in review texts and simultaneously predict their sentiment polarity. It is an important fine-grained subtask in the field of sentiment analysis. Currently, fine-tuning with pretrained language models shows effectiveness in ACSA, but its training tasks are different from downstream ACSA, which limits its analysis quality. Although the prompt template based prompt learning shows its good performance, it is not diverse enough to cover different contexts in the manually designed prompts for ACSA. To solve this problem, this paper proposes an aspect category sentiment analysis method (Multi-Prompt_ACSA) based on prompt learning. Firstly, on the basis of prompt learning, the diversified design of prompt template engineering and answer engineering is carried out. Based on the characteristics of ACSA task, a prompt learning method is proposed to match aspect-category sentiment analysis. Then, an autoregressive pretrained language model is introduced. Further, classification results are integrated based on Prompt??s diverse design. Compared with the results of other models including non-pretrain, pretrain and prompt learning, on the SemEval 2015 and SemEval 2016 datasets, the proposed prompt-based learning method has fine improvement in terms of F1.

Keywords

• aspect-category sentiment analysis; prompt learning; diversified prompt design