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Leveraging Generative Artificial Intelligence for Enhanced Data Augmentation in Emotion Intensity Classification: A Comprehensive Framework for Cross-Dataset Transfer Learning

2026-03-03 health informatics Title + abstract only
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Data scarcity and stylistic heterogeneity pose major challenges for emotion intensity classification. This paper presents a cross-dataset augmentation framework that leverages prompt-conditioned generative models alongside deterministic and heuristic transformations to synthesize target-style examples for improved transfer learning. We introduce a unified taxonomy of augmentation strategies--Heuristic Lexical Perturbation (HLA), Prompt-Conditioned Generative Augmentation (CGA), Sequential Hybrid...

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