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.
@article{arxiv.2004.00300,
title = {Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?},
author = {Nicole Novielli and Fabio Calefato and Davide Dongiovanni and Daniela Girardi and Filippo Lanubile},
journal= {arXiv preprint arXiv:2004.00300},
year = {2020}
}