Abstract: Addressing the problem where fine-tuning-based methods for pre-trained language models perform poorly in few-shot learning scenarios for sentiment classification, this paper proposes a few-shot sentiment classification method based on improved prompt learning and prototype label mapping. When constructing prompt templates using prompt learning, keywords from the original text are integrated to increase the weight of key information’s impact on the label results. Subsequently, during the label mapping process, prototype networks are introduced to learn prototype vectors of different categories. The model’s prediction results are then mapped to the specific labels based on these learned prototype vectors. Experimental results on the EPRSTMT and SST-2 datasets show that the  model of the proposed method achieves average accuracy rates of 88.7% and 91.9% in few-shot scenarios. Compared to fine-tuning methods, the model of the proposed method improves the accuracy by 15.5% and 14.0%, respectively. Compared to the P-Tuning method, our method improves the accuracy by 2.1% and 0.7%. The experimental results validate the effectiveness of the proposed method for sentiment classification in few-shot scenarios.

Key words: pre-trained language model, sentiment classification, prompt learning, keyword extraction