Advancing Emotional Analysis with Large Language Models
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Abstract
The objective of this research is to enhance the efficiency of intelligence acquisition through sentiment analysis of public opinion, a crucial element of open-source intelligence, utilizing a few-shot learning framework. To address the limitations of sentiment detection models in low-resource environments due to insufficient data, we propose a novel method that integrates large language model knowledge with contrastive prompts. The methodology begins with augmenting training samples using the general knowledge from large language models. This is followed by employing unlabeled data for contrastive embedding training to improve the semantic representation capabilities of the text encoder. Finally, a prompt-based learning mechanism is used for iterative self-prediction training on the unlabeled data, further refining the model for specific tasks. Experimental results on public datasets demonstrate that the proposed model outperforms baseline methods when the same amount of labeled data is used.
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