arXiv Computation and Language By Christopher Bagdon, Carina Silberer, Roman Klinger

Emotion Experience, Expression, and Perception: Emotion Analysis on Multimodal Social Media Posts

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The paper introduces the Mult2EMo dataset, which gathers annotations from both authors and readers on multimodal social media posts and the real‑world events that triggered them. It investigates how well readers can reconstruct the authors’ emotional experience from the post content, emphasizing the importance of both text and image modalities. The study finds that accurate emotion reconstruction is possible but remains challenging, especially when images dominate the expression and when understanding the triggering event is essential.

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arXiv AI
Oct 28

CLEAR: Coupling Local Evidence with Affective Representations for Multimodal Emotion Recognition in Conversation

The paper introduces CLEAR, a framework for Multimodal Emotion Recognition in Conversation that couples contextual representations with local discriminative cues. It replaces emotion label generation with continuous internal representations from a vision‑language model and trains a classifier on these representations. Sample‑adaptive residual coupling is used to integrate textual and acoustic evidence from the target utterance into the contextual representation, improving emotion recognition performance on IEMOCAP and MELD datasets.

By Linan Zhu, Zihao Zhai, Mengfei Xu, Xiao Han, Yuqian Fu, Xiangfan Chen, Zihang Zhang