Sentiment Polarity Detection for Software Development
This work addresses sentiment analysis for software developers' communications, offering a domain-specific tool to improve accuracy in technical contexts, though it is incremental as it adapts existing methods to a new domain.
The authors tackled the problem of off-the-shelf sentiment analysis tools misclassifying technical jargon in software developers' communications by developing Senti4SD, a classifier trained on a manually annotated gold standard from Stack Overflow, which reduced misclassifications of neutral and positive posts as negative compared to a baseline tool.
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.