In the era of artificial intelligence, data is gold but costly to annotate. The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT for text augmentation in sentiment analysis. We leverage ChatGPT's generative capabilities to create synthetic training data that significantly improves the performance of smaller models, making them competitive with, or even outperforming, their larger counterparts. This innovation enables models to be both efficient and effective, thereby reducing computational cost, inference time, and memory usage without compromising on quality. Our work marks a key advancement in the cost-effective development and deployment of robust sentiment analysis models.

翻译：在人工智能时代，数据如金但标注成本高昂。本文展示了一种突破性解决方案，利用ChatGPT在情感分析中进行文本增强。我们借助ChatGPT的生成能力创建合成训练数据，显著提升了小型模型的性能，使其能与甚至超越大型模型。这一创新使模型兼具高效性与有效性，从而在降低计算成本、推理时间和内存占用的同时，不牺牲质量。我们的工作标志着经济高效地开发和部署稳健情感分析模型的关键进展。