Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

• URL: http://arxiv.org/abs/2310.11113v2
• Date: Thu, 19 Oct 2023 13:16:38 GMT
• Title: Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models
• Authors: Ting Zhang and Ivana Clairine Irsan and Ferdian Thung and David Lo
• Abstract summary: We study the performance of three open-source bLLMs in both zero-shot and few-shot scenarios. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions.
• Score: 12.440597259254286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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