# Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?

arXiv: https://arxiv.org/abs/2004.00300 · HTML: https://mlsanity.com/paper/2004.00300/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers](https://mlsanity.com/paper/2207.03277/) (2022)
- [On the validity of pre-trained transformers for natural language processing in the software engineering domain](https://mlsanity.com/paper/2109.04738/) (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)
- [How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses](https://mlsanity.com/paper/2602.17084/) (2026)
- [Assessing the Use of AutoML for Data-Driven Software Engineering](https://mlsanity.com/paper/2307.10774/) (2023)
- [Sentiment Analysis in Software Engineering: Evaluating Generative Pre-trained Transformers](https://mlsanity.com/paper/2505.14692/) (2025)
- [Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?](https://mlsanity.com/paper/2604.13826/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of using sentiment analysis tools 'off-the-shelf,' that is when a gold standard is not available for retraining. We evaluate the performance of four SE-specific tools in a cross-platform setting, i.e., on a test set collected from data sources different from the one used for training. We find that (i) the lexicon-based tools outperform the supervised approaches retrained in a cross-platform setting and (ii) retraining can be beneficial in within-platform settings in the presence of robust gold standard datasets, even using a minimal training set. Based on our empirical findings, we derive guidelines for reliable use of sentiment analysis tools in software engineering.