Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?
MSR - Technical Paper
We address the problem of using sentiment analysis tools ‘off-the-shelf’, that is when a gold standard is not available for re-training. We evaluate the performance of 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.