  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 Pre-trained Language Models
(PTMs) for fine-grained emotion classification on two benchmark datasets from
GitHub and Stack Overflow. We evaluate six transformer models - BERT, RoBERTa,
ALBERT, DeBERTa, CodeBERT and GraphCodeBERT against the current best-performing
tool SEntiMoji. Our analysis reveals consistent improvements ranging from 1.17%
to 16.79% in terms of macro-averaged and micro-averaged F1 scores, with general
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 PTMs approaches. Our
work provides strong evidence for the advancements afforded by PTMs in
recognizing nuanced emotions like Anger, Love, Fear, Joy, Sadness, and Surprise
in software engineering contexts. Through comprehensive benchmarking and error
analysis, we also outline scope for improvements to address contextual gaps.
