From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Text
arXiv:2606.16687v1 Announce Type: cross Abstract: Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting. Existing approaches often treat each text as an independent observation and apply similar assumptions to both tasks, without testing whether they rely on different information sources. This paper investigates that distinction using longitudinal self-reported ecological essays and feeling-word entries. We propose the Trait--State Affective Prediction (TSAP) framework and its temporal extension E-TSAP for pe
The proliferation of longitudinal text data and advancements in AI/NLP make affect analysis increasingly viable, necessitating more sophisticated models to differentiate current states from future predictions.
This research refines the understanding of emotional AI, crucial for applications in mental health, customer service, and human-computer interaction, by identifying distinct data requirements for real-time versus predictive affect analysis.
Approaches to affective computing will need to clearly distinguish between current affect estimation and future affect forecasting, potentially leading to more accurate and nuanced AI applications in emotional intelligence.
- · AI developers (NLP, affective computing)
- · Mental health tech companies
- · Customer experience platforms
- · Personalized AI assistants
- · Generic emotion detection models
- · Applications that conflate current and future affect prediction
Improved accuracy in AI systems that analyze and respond to human emotions, leading to more context-aware interactions.
Development of personalized AI interventions for mental well-being and productivity based on forecasted emotional states.
Ethical debates intensify regarding the use of AI for predicting and potentially influencing human emotional trajectories.
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