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• W4313563595 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." @default.
• W4313563595 created "2023-01-06" @default.
• W4313563595 creator A5049106181 @default.
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• W4313563595 date "2022-10-10" @default.
• W4313563595 modified "2023-10-10" @default.
• W4313563595 title "Data Augmentation for Improving Emotion Recognition in Software Engineering Communication" @default.
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• W4313563595 doi "https://doi.org/10.1145/3551349.3556925" @default.
• W4313563595 hasPublicationYear "2022" @default.
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