Data Augmentation for Improving Emotion Recognition in Software Engineering Communication
Mia Mohammad Imran, Yashasvi Jain, Preetha Chatterjee, Kostadin Damevski
Abstract
Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classification tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one communication channel and evaluated on another (e.g, StackOverflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating a large dataset of ground truth data is expensive.
Topics & Concepts
RetrainingComputer scienceSoftwareGround truthAngerDomain (mathematical analysis)Channel (broadcasting)Emotion recognitionArtificial intelligenceNatural language processingHuman–computer interactionMachine learningProgramming languagePsychiatryInternational tradePsychologyMathematicsComputer networkBusinessMathematical analysisTopic ModelingSentiment Analysis and Opinion MiningSoftware Engineering Research