计算机科学与探索2025，Vol.19Issue(5)：1322-1333,12.DOI:10.3778/j.issn.1673-9418.2406005
基于PPLM模板增强的零样本方面类别情感分析模型
Zero-Shot Aspect Category Sentiment Analysis Model with Enhanced PPLM Tem-plate
摘要
Abstract
Aspect category sentiment analysis(ACSA)is currently constrained by the scarcity of annotated data,making it a research challenge to achieve effective analysis without specific sentiment-labeled data.This paper transforms the zero-shot ACSA task into a natural language inference(NLI)task.Addressing the issue of inadequate semantic expression in traditional prompt templates,this paper proposes a cause supplementation prompt template based on the plug and play lan-guage model(PPLM)for text restriction generation.By combining sentiment polarity with its causes,the template helps the model better understand the reasons and motivations behind the sentiments,thereby improving the accuracy and inter-pretability of sentiment analysis.Furthermore,to enhance the classification performance of ACSA,the performance inver-sion coefficient is introduced to determine the ensemble weights of various prompt templates in the paper.Experimental results on the public datasets MAMS and Restaurant demonstrate that the proposed model outperforms other zero-shot ACSA models by approximately 7 percentage points in accuracy.The PPLM cause supplementation prompt template can enhance the zero-shot classification performance of NLI models,showing a 2.6 percentage points improvement in Macro F1 score compared with other better traditional templates.Additionally,the improved weight determination strategy also contributes to the model·s sentiment analysis capability in zero-shot scenarios.关键词
方面类别情感分析/零样本学习/提示模板/PPLM文本限制生成Key words
aspect category sentiment analysis/zero-shot learning/prompt template/PPLM text-conditioned generation分类
信息技术与安全科学引用本文复制引用
凤丽洲,李梦莎,王友卫,杨贵军..基于PPLM模板增强的零样本方面类别情感分析模型[J].计算机科学与探索,2025,19(5):1322-1333,12.基金项目
国家社科一般项目(20BTJ058) （20BTJ058）
天津市研究生科研创新项目(2022SKY349). This work was supported by the National Social Science Fund General Project of China(20BTJ058),and the Research Innovation Project for Postgraduate Students in Tianjin(2022SKY349). （2022SKY349）
