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Emotion recognition in software engineering texts is critical forunderstanding developer expressions and improving collaboration. This paperpresents a comparative analysis of state-of-the-art Pre-trained Language Models(PTMs) for fine-grained emotion classification on two benchmark datasets fromGitHub and Stack Overflow. We evaluate six transformer models - BERT, RoBERTa,ALBERT, DeBERTa, CodeBERT and GraphCodeBERT against the current best-performingtool SEntiMoji. Our analysis reveals consistent improvements ranging from1.17\% to 16.79\% in terms of macro-averaged and micro-averaged F1 scores, withgeneral domain models outperforming specialized ones. To further enhance PTMs,we incorporate polarity features in attention layer during training,demonstrating additional average gains of 1.0\% to 10.23\% over baseline PTMsapproaches. Our work provides strong evidence for the advancements afforded byPTMs in recognizing nuanced emotions like Anger, Love, Fear, Joy, Sadness, andSurprise in software engineering contexts. Through comprehensive benchmarkingand error analysis, we also outline scope for improvements to addresscontextual gaps.

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