Text Sentiment Classification Algorithm Based on Prompt Learning Enhancement
[Objective]This paper aims to improve the low accuracy of sentiment classification using the pre-trained model with insufficient samples.[Methods]We proposed a prompt learning enhanced sentiment classification algorithm Pe(prompt ensemble)-RoBERTa.It modified the RoBERTa model with integrated prompts different from the traditional fine-tuning methods.The new model could understand the downstream tasks and extract the text's sentiment features.[Results]We examined the model on several publicly accessible Chinese and English datasets.The average sentiment classification accuracy of the model reached 93.2％with fewer samples.Compared with fine-tuned and discrete prompts,our new model's accuracy improved by 13.8％and 8.1％,respectively.[Limitations]The proposed model only processes texts for the sentiment dichotomization tasks.It did not involve the more fine-grained sentiment classification tasks.[Conclusions]The Pe-RoBERTa model can extract text sentiment features and achieve high accuracy in sentiment classification tasks.
NETL
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