Affective Forecasting, a research direction in psychology that predicts individuals future emotions, is often constrained by numerous external factors like social influence and temporal distance. To address this, we transform Affective Forecasting into a Deep Learning problem by designing an Emotion Forecasting paradigm based on two-party interactions. We propose a novel Emotion Forecasting (EF) task grounded in the theory that an individuals emotions are easily influenced by the emotions or other information conveyed during interactions with another person. To tackle this task, we have developed a specialized dataset, Human-interaction-based Emotion Forecasting (Hi-EF), which contains 3069 two-party Multilayered-Contextual Interaction Samples (MCIS) with abundant affective-relevant labels and three modalities. Hi-EF not only demonstrates the feasibility of the EF task but also highlights its potential. Additionally, we propose a methodology that establishes a foundational and referential baseline model for the EF task and extensive experiments are provided. The dataset and code is available at https://github.com/Anonymize-Author/Hi-EF.

翻译：情感预测作为心理学中预测个体未来情绪的研究方向，常受社会影响、时间距离等诸多外部因素制约。为应对此问题，我们将情感预测转化为深度学习问题，设计了一种基于双方交互的情感预测范式。基于"个体情绪易受互动对象传递的情绪或其他信息影响"的理论，我们提出了一种新颖的情感预测任务。针对该任务，我们构建了专用数据集——基于人际交互的情感预测数据集，其中包含3069个双方多层情境交互样本，涵盖丰富的情感相关标注与三种模态。该数据集不仅验证了情感预测任务的可行性，更凸显了其潜在价值。此外，我们提出了一套方法论，为情感预测任务建立了基础性参考基准模型，并提供了详实的实验验证。数据集与代码已公开于https://github.com/Anonymize-Author/Hi-EF。