论文标题
我们可以在跨平台设置中使用特定的情感分析工具吗？
Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?
论文作者
论文摘要
在本文中，我们解决了使用情感分析工具“现成”的问题，即无法获得金标准的时候。我们在跨平台设置中评估了四个SE特异性工具的性能，即从与用于培训的数据源收集的测试集中。我们发现（i）基于词典的工具优于跨平台设置中的监督方法再训练，并且（ii）在存在强大的金标准数据集的情况下，即使使用最少的训练集，则在平台内设置中进行了重新训练。根据我们的经验发现，我们得出了用于可靠使用情感分析工具在软件工程中的准则。
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.
