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What is Evaluating PLMs for Sentiment Analysis in SE about?

This paper presents a comprehensive evaluation of pretrained language models (PLMs) for sentiment analysis in software engineering, comparing four traditional fine-tuned PLMs, five zero-shot PLMs including GPT-4, and three few-shot PLMs across six domain-specific datasets. The findings indicate that the traditionally fine-tuned seBERT performs best on larger datasets, while few-shot models excel on smaller datasets, and an error analysis reveals challenges faced by PLMs in this domain. The study contributes

Author

gee.ahmed.ali

Language

EN