  Software development is an inherently collaborative process, where various
stakeholders frequently express their opinions and emotions across diverse
platforms. Recognizing the sentiments conveyed in these interactions is crucial
for the effective development and ongoing maintenance of software systems. Over
the years, many tools have been proposed to aid in sentiment analysis, but
accurately identifying the sentiments expressed in software engineering
datasets remains challenging.
  Although fine-tuned smaller large language models (sLLMs) have shown
potential in handling software engineering tasks, they struggle with the
shortage of labeled data. With the emergence of bigger large language models
(bLLMs), it is pertinent to investigate whether they can handle this challenge
in the context of sentiment analysis for software engineering. In this work, we
undertake a comprehensive empirical study using five established datasets. We
assess the performance of three open-source bLLMs in both zero-shot and
few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs.
  Our experimental findings demonstrate that bLLMs exhibit state-of-the-art
performance on datasets marked by limited training data and imbalanced
distributions. bLLMs can also achieve excellent performance under a zero-shot
setting. However, when ample training data is available or the dataset exhibits
a more balanced distribution, fine-tuned sLLMs can still achieve superior
results.
