Emotion analysis of dialogue text based on ChatGPT: a research study

Cunzhou Ran

doi:10.1117/12.3011507

7 December 2023 Emotion analysis of dialogue text based on ChatGPT: a research study

Cunzhou Ran

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294137 (2023) https://doi.org/10.1117/12.3011507
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Emotions are important signals in human communication in daily life and are also considered a crucial factor in human-machine interaction. Conducting emotion analysis based on text data holds significant importance for expanding the scope of machine applications and enhancing interactivity. In this paper, we conduct emotion analysis research using the public Twitter dataset, focusing on two widely used models in the current NLP field for emotion analysis: the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). Additionally, we explore the application of large pre-trained language models, represented by ChatGPT, in the domain of emotion analysis. The experimental results indicate that while CNN and BiLSTM models achieve relatively high accuracy in supervised emotion analysis, ChatGPT demonstrates superior performance in unsupervised learning for emotion analysis. Furthermore, the exploration of emotion analysis based on ChatGPT reveals some previously unnoticed issues.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cunzhou Ran "Emotion analysis of dialogue text based on ChatGPT: a research study", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294137 (7 December 2023); https://doi.org/10.1117/12.3011507

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