# Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?

arXiv: https://arxiv.org/abs/2604.13826 · HTML: https://mlsanity.com/paper/2604.13826/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [A Benchmark Study on Sentiment Analysis for Software Engineering Research](https://mlsanity.com/paper/1803.06525/) (2018)
- [Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models](https://mlsanity.com/paper/2310.11113/) (2023)
- [Sentiment Polarity Detection for Software Development](https://mlsanity.com/paper/1709.02984/) (2017)
- [BERT-Based Sentiment Analysis: A Software Engineering Perspective](https://mlsanity.com/paper/2106.02581/) (2021)
- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)
- [Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?](https://mlsanity.com/paper/2004.00300/) (2020)
- [Data Augmentation for Improving Emotion Recognition in Software Engineering Communication](https://mlsanity.com/paper/2208.05573/) (2022)
- [Development and Application of Sentiment Analysis Tools in Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2105.02703/) (2021)
- [An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets](https://mlsanity.com/paper/2111.03196/) (2021)
- [Sentiment Analysis Tools in Software Engineering: A Systematic Mapping Study](https://mlsanity.com/paper/2502.07893/) (2025)
- [Towards Understanding Emotions in Informal Developer Interactions: A Gitter Chat Study](https://mlsanity.com/paper/2311.04755/) (2023)

## Abstract (arXiv)

Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limitations of applying general off-the-shelf sentiment analysis tools within the software engineering domain and indicated the need for specialized tools tailored to various software engineering contexts. The development of such tools heavily relies on supervised machine learning techniques that necessitate annotated datasets. Acquiring such datasets is a substantial challenge, as it requires domain-specific expertise and significant effort. Objective: This study explores the potential of ZSL to address the scarcity of annotated datasets in sentiment analysis within software engineering Method:} We conducted an empirical experiment to evaluate the performance of various ZSL techniques, including embedding-based, NLI-based, TARS-based, and generative-based ZSL techniques. We assessed the performance of these techniques under different labels setups to examine the impact of label configurations. Additionally, we compared the results of the ZSL techniques with state-of-the-art fine-tuned transformer-based models. Finally, we performed an error analysis to identify the primary causes of misclassifications. Results: Our findings demonstrate that ZSL techniques, particularly those combining expert-curated labels with embedding-based or generative-based models, can achieve macro-F1 scores comparable to fine-tuned transformer-based models. The error analysis revealed that subjectivity in annotation and polar facts are the main contributors to ZSL misclassifications. Conclusion: This study demonstrates the potential of ZSL for sentiment analysis in software engineering. ZSL can provide a solution to the challenge of annotated dataset scarcity by reducing reliance on annotated dataset.