Zero-Shot Aspect Category Sentiment Analysis Model with Enhanced PPLM Template

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 language 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 interpretability of sentiment analysis. Furthermore, to enhance the classification performance of ACSA, the performance inversion 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.