  The role of sentiment analysis is increasingly emerging to study software
developers' emotions by mining crowd-generated content within social software
engineering tools. However, off-the-shelf sentiment analysis tools have been
trained on non-technical domains and general-purpose social media, thus
resulting in misclassifications of technical jargon and problem reports. Here,
we present Senti4SD, a classifier specifically trained to support sentiment
analysis in developers' communication channels. Senti4SD is trained and
validated using a gold standard of Stack Overflow questions, answers, and
comments manually annotated for sentiment polarity. It exploits a suite of both
lexicon- and keyword-based features, as well as semantic features based on word
embedding. With respect to a mainstream off-the-shelf tool, which we use as a
baseline, Senti4SD reduces the misclassifications of neutral and positive posts
as emotionally negative. To encourage replications, we release a lab package
including the classifier, the word embedding space, and the gold standard with
annotation guidelines.
