Sentiment Polarity Detection for Software Development (1709.02984v2)

Abstract: 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.

Sentiment Polarity Detection for Software Development

The paper entitled “Sentiment Polarity Detection for Software Development” by Calefato et al. introduces a novel sentiment analysis tool named Senti4SD, tailored for the software engineering domain. Standard sentiment analysis tools face challenges due to their general-purpose training on non-technical text, which often leads to inappropriate sentiment classification of technical jargon common within software development contexts. The research addresses this gap by proposing a sentiment classifier specifically calibrated for the sentiment nuances found in software development communications, with a particular focus on content from Stack Overflow.

Senti4SD is rigorously developed and validated using a gold standard comprised of 4,423 posts from Stack Overflow—meticulously annotated for sentiment polarity. The authors employ a mixture of lexicon-based, keyword-based, and semantic features for the classifier, aiming to mitigate the typical misclassification of neutral technical posts as negative, a prevalent issue with off-the-shelf sentiment analysis tools such as SentiStrength. Empirical results demonstrate significant improvements: a 19% increase in negative class precision and a 25% enhancement in neutral class recall over SentiStrength. This improvement underscores Senti4SD’s capability to differentiate between actual sentiment and domain-specific contextual language.

The framework leverages sophisticated natural language processing techniques, including distributional semantics via word embedding models generated through word2vec. The resulting Distributional Semantic Model (DSM) encompasses a vast amount of developer-oriented text, offering a domain-specific foundation for nuanced sentiment analysis.

Implications and Future Research Directions

The implications of such a classifier are manifold. Practically, sentiment analysis tools like Senti4SD could be incorporated into development environments (e.g., GitHub, Jira) to provide insights into the emotional dynamics of collaborative software engineering. This might include early warning systems for negative dynamics in team communications or identifying particularly challenging problem domains that trigger strong emotional responses. From a theoretical standpoint, the paper contributes to the methodology of specialized sentiment analysis, providing evidence that domain-specific models considerably outperform generalized tools in domain-specific scenarios.

The research opens several avenues for future exploration. Senti4SD provides the groundwork for more sophisticated emotional analysis in software communications, potentially training models to identify more granular emotions beyond polarity. Expanding the analysis to include other repositories and social software engineering tools remains an unexplored domain offering ample opportunities for further improving emotion recognition in text.

With the release of Senti4SD, its accompanying gold standard, and the annotation guidelines, the authors encourage further replication and validation of their approach, which they hope will advance emotion-aware tools in software engineering. The advancements in sentiment analysis techniques as demonstrated by Senti4SD set a benchmark, potentially informing the design of future automated tools and methodologies in software engineering research and practice.