This technical report explores the ability of ChatGPT in recognizing emotions from text, which can be the basis of various applications like interactive chatbots, data annotation, and mental health analysis. While prior research has shown ChatGPT's basic ability in sentiment analysis, its performance in more nuanced emotion recognition is not yet explored. Here, we conducted experiments to evaluate its performance of emotion recognition across different datasets and emotion labels. Our findings indicate a reasonable level of reproducibility in its performance, with noticeable improvement through fine-tuning. However, the performance varies with different emotion labels and datasets, highlighting an inherent instability and possible bias. The choice of dataset and emotion labels significantly impacts ChatGPT's emotion recognition performance. This paper sheds light on the importance of dataset and label selection, and the potential of fine-tuning in enhancing ChatGPT's emotion recognition capabilities, providing a groundwork for better integration of emotion analysis in applications using ChatGPT.

翻译：本技术报告探讨了ChatGPT从文本中识别情感的能力，这是交互式聊天机器人、数据标注和心理健康分析等多种应用的基础。尽管先前研究已表明ChatGPT在情感分析方面具备基础能力，但其在更细微情感识别任务中的表现尚未得到充分探索。我们通过实验评估了ChatGPT在不同数据集和情感标签下的情感识别性能。研究结果表明，其性能具有合理水平的可复现性，并且通过微调可显著提升。然而，性能会因情感标签和数据集的差异而波动，暴露出固有的不稳定性及潜在偏差。数据集和情感标签的选择对ChatGPT的情感识别性能具有显著影响。本文揭示了数据集与标签选择的重要性，以及微调在增强ChatGPT情感识别能力方面的潜力，为在基于ChatGPT的应用中更好地整合情感分析奠定了基础。