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This paper introduces NTDH, a complex-reasoning framework for comprehensive affective analysis that unifies sentiment and emotion prediction tasks. Trained with SFT and GRPO on Qwen3-8B, it achieves strong results on SemEval-2018 EI-reg with a Pearson correlation of 0.862.
This paper compares generation and judgement paradigms for LLM-based emotion-cause pair extraction in conversation, finding that pair-level judgement outperforms dialogue-level generation, and introduces an auxiliary retriever for improvement.
This paper proposes an emotion analysis interface using Natural Semantic Metalanguage (NSM) to generate faithful, interpretable explanations for emotion classifications, trading slight accuracy for verifiability.
This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms, using a comparative study of Margaret Atwood's 'Oryx and Crake' translated to Italian.