Emotion Classification In Software Engineering Texts: A Comparative Analysis of Pre-trained Transformers Language Models

Emotion recognition in software engineering texts is critical for understanding developer expressions and improving collaboration. This paper presents a comparative analysis of state-of-the-art Pretrained Language Models (PTMs) for fine-grained emotion classification on two benchmark datasets from G...

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Veröffentlicht in:	2024 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE) S. 73 - 80
1. Verfasser:	Imran, Mia Mohammad
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 20.04.2024
Schlagworte:	ALBERT Analytical models Benchmark testing BERT CodeBERT DeBERTa Emotion Classification Emotion recognition GraphCodeBERT Large Language Models RoBERTa Software Task analysis Training Transformers
Online-Zugang:	Volltext
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