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

arXiv: https://arxiv.org/abs/2310.11113 · HTML: https://mlsanity.com/paper/2310.11113/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2023.

## Built on by (most cited first, 13 of 13)

- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs](https://mlsanity.com/paper/2406.09136/) (2024)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA](https://mlsanity.com/paper/2405.07101/) (2024)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Large Language Models' Detection of Political Orientation in Newspapers](https://mlsanity.com/paper/2406.00018/) (2024)
- [Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research](https://mlsanity.com/paper/2506.12691/) (2025)
- [Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical Concern-related App Reviews](https://mlsanity.com/paper/2411.07398/) (2024)
- [Correspondence of high-dimensional emotion structures elicited by video clips between humans and Multimodal LLMs](https://mlsanity.com/paper/2505.12746/) (2025)
- [Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models](https://mlsanity.com/paper/2601.05663/) (2026)
- [ReDef: Do Code Language Models Truly Understand Code Changes for Just-in-Time Software Defect Prediction?](https://mlsanity.com/paper/2509.09192/) (2025)
- [SpaceX: Exploring metrics with the SPACE model for developer productivity](https://mlsanity.com/paper/2511.20955/) (2025)
- [Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?](https://mlsanity.com/paper/2604.13826/) (2026)

## Abstract (arXiv)

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. 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.