  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.
