Improving emotion estimation through a combination of ChatGPT and deep learning
Yusuke Sekine, Seiji Kasuya, Kiichi Tago · Journal of Decision System · 2025
Estimating emotions from text is essential for machines to make human-like decisions, but it remains a challenge due to the significant time and manpower required for labeling training data. Accurate machine learning relies on high-quality training data, often necessitating multiple rounds of evaluation for emotion annotation. Large Language Models (LLMs), such as ChatGPT, hold promise for reducing the labor involved in this process. This study investigates how LLMs can minimize effort while improving accuracy. In Experiment 1, the performance of LLMs in emotion estimation was assessed, showing strong accuracy for negative emotions despite some inconsistencies with human annotations. Experiment 2 introduced a hybrid approach combining LLMs with deep learning techniques to address these inconsistencies, resulting in significantly improved accuracy. These findings demonstrate that integrating LLMs into emotion annotation workflows can reduce manual effort and enhance accuracy, offering a promising pathway for machine learning applications in emotion estimation.