发表机构
University of Bamberg; University of Stuttgart（班贝格大学; 斯图加特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出Mult2EMo数据集，探究社交媒体帖子中作者情感体验与读者感知的关系，发现重建情感表达具挑战性，理解触发事件至关重要。
AI 中文摘要
情感是人类交流的重要方面，尤其是在社交媒体上，作者经常结合文本和图像来传达情感。然而，先前关于社交媒体帖子情感分析的工作忽视了衡量读者重建作者意图能力的两个重要方面：（1）图像模态，大多数工作仅关注文本；（2）触发所表达情感的真实世界事件及其与帖子内容的关系。因此，我们研究了（a）作者对导致其撰写社交媒体帖子的事件的体验与（b）帖子内容之间的关系，重点关注读者重建该情感表达的能力。为此，我们引入了多模态多情感模型数据集Mult2EMo，该数据集通过收集作者和读者对帖子及其触发事件的标注而创建。我们发现，对于人类读者和计算模型而言，重建是可能的但具有挑战性。我们表明，理解触发事件对于准确重建至关重要，并且当帖子严重依赖图像来表达情感时，重建尤其具有挑战性。
英文摘要
Emotions are an essential aspect of human communication, particularly on social media, where authors frequently combine text and images to convey their emotions. Yet prior work on emotion analysis of social media posts has overlooked two important aspects in regard to measuring how well readers can reconstruct the authors' intent: (1)~the image modality, with most work focusing solely on text, and (2)~the real-world events that trigger the expressed emotions, and their relationship to the post content. We therefore study the relation between (a) the author's experience of the event that caused them to write a social media post and (b) the content of the post, with a focus on readers' capability to reconstruct that emotion expression. To do that, we introduce the Multimodal Multi-Emotion-Model dataset Mult2EMo, created by collecting annotations from both authors and readers on the posts and their triggering events. We find that reconstruction is possible but challenging for both human readers and computational models. We show that understanding the triggering event is crucial for accurate reconstruction, and that reconstruction is particularly challenging when posts rely heavily on the image to express emotion.
CommentsAccepted for publication at EMNLP 2026 main conference
