Abstract
Sentiment Analysis (SA) in software engineering (SE) text has drawn immense interests recently. The poor performance of general-purpose SA tools, when operated on SE text, has led to recent emergence of domain-specific SA tools especially designed for SE text. However, these domain-specific tools were tested on single dataset and their performances were compared mainly against general-purpose tools. Thus, two things remain unclear: (i) how well these tools really work on other datasets, and (ii) which tool to choose in which context. To address these concerns, we operate three recent domain-specific SA tools on three separate datasets. Using standard accuracy measurement metrics, we compute and compare their accuracies in the detection of sentiments in SE text.
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