SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text

Using a benchmark dataset consisting of 5,600 manually annotated JIRA issue comments, we carry out both qualitative and quantitative evaluations of our tool. We also separately evaluate the contributions of individual major components (i.e., domain dictionary and heuristics) of SentiStrength-SE. The empirical evaluations confirm that the domain specificity exploited in our SentiStrength-SE enables it to substantially outperform the existing domain-independent tools/toolkits (SentiStrength, NLTK, and Stanford NLP) in detecting sentiments in software engineering text.